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To: Walter Logeman <walter@PSYBERNET.CO.NZ>
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Date:         Sat, 5 Feb 2000 14:09:39 +1300
Reply-To:     Exploring the Psyche in Cyberspace <PSYBER-L@HOME.EASE.LSOFT.COM>
Sender:       Exploring the Psyche in Cyberspace <PSYBER-L@HOME.EASE.LSOFT.COM>
From:         Walter Logeman <walter@PSYBERNET.CO.NZ>
Organization: psybernet
Subject:      Hosting, psychological work, and Psyber-l
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I am a regular visitor in an online web based community, maybe
even more than a visitor, I have a sense I belong.

I got a post today reminding me i have not logged on for a month
and that participation was important.

Whew!

I have been deeply involved in the Dreamevent [*** see footnote]
over what is a summer period here and find it hard to be in so
many places at once.

I am not sure if I resented the reminder to be involved (in the
other community) or not. I am sure nudges like that make the
place work well...  as well as the fact that non-participants get
removed from the list.  It is a place i want to be! I've logged
on already, and checked out what's happening.

I am thinking about Psyber-L.  I have an idea that the quiet is
OK, that we do not put *expectations* on people to participate.

However I notice that when i go for a while psyber-l goes quiet!

Could this be a time that others have a go?  At "hosting". It is
the aim of psyber-l to be a forum for people involved in
psychological work on the net, to learn about that
experientially... this is a good place to do that!


Walter


[ *** The Dreamevent is a closed group, at this point... you can
see the description for it at:  The Invitation, Description and
Guideline doc on the Web:
http://www.psybernet.co.nz/de99description.htm ]

__________________________________________________________________
Walter Logeman  walter@psybernet.co.nz  http://www.psybernet.co.nz

____________________________________________________________
Read, write in Psyber-L, manage your options, or unsubscribe
here:      http://home.ease.lsoft.com/archives/psyber-l.html


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Date:         Fri, 11 Feb 2000 12:11:37 +1300
Reply-To:     Exploring the Psyche in Cyberspace <PSYBER-L@HOME.EASE.LSOFT.COM>
Sender:       Exploring the Psyche in Cyberspace <PSYBER-L@HOME.EASE.LSOFT.COM>
From:         Walter Logeman <walter@PSYBERNET.CO.NZ>
Organization: psybernet
Subject:      Cluetrain Manifesto
Comments: To: group-l@lists.groupsense.co.nz, josh@zesty.com,
          maddie@gropusense.co.nz
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Gp-L, Psyber-L, Josh, Maddie,

Funny how some things just come at me from three or four
directions at once... this train just hit me like that in the
last day.  Finally had to have a look.

http://www.cluetrain.com/

The first item on the list makes me sit up:

1.  Markets are conversations.

However this is Big Buzz stuff... like technorealism, libertarian
ideology, immediately, even without studying any of this yet, I
want to write:

Revenge of the Clueless!



Walter


__________________________________________________________________
Walter Logeman  walter@psybernet.co.nz  http://www.psybernet.co.nz

____________________________________________________________
Read, write in Psyber-L, manage your options, or unsubscribe
here:      http://home.ease.lsoft.com/archives/psyber-l.html


=========================================================================
Date:         Sat, 12 Feb 2000 13:35:03 +1300
Reply-To:     Exploring the Psyche in Cyberspace <PSYBER-L@HOME.EASE.LSOFT.COM>
Sender:       Exploring the Psyche in Cyberspace <PSYBER-L@HOME.EASE.LSOFT.COM>
From:         Walter Logeman <walter@PSYBERNET.CO.NZ>
Organization: psybernet
Subject:      Visualizing Social Networks
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All in Psyber-L,


Just got this URL from Josh...

http://www.heinz.cmu.edu/project/INSNA/joss/vsn.html


It must be seen on the web as the images are important, however I
have included the text below, for convenience.


Walter


<quote>

Visualizing Social Networks

Linton C. Freeman
University of California, Irvine

1. Introduction: Imagery in Social Network Analysis

The use of visual images is common in many branches of science.
And reviewers often suggest that such images are important for
progress in the various fields (Koestler, 1964; Arnheim, 1970;
Taylor, 1971; Tukey, 1972; Klovdahl, 1981; Tufte, 1983; Belin
and Leenders). The historian Alfred Crosby (1997) has gone much
further. He has proposed that visualization is one of only two
factors that are responsible for the explosive development of all
of modern science. The other is measurement.

These two factors seem to have been central in the growth of
social network analysis. Advances in measurement have been both
rapid and regular in social network research (Wasserman and
Faust, 1994). And, from the beginning, visual images have played
a key role in the field (Wolfe, 1978; Hage and Harary, 1983).
Images of social networks have provided investigators with new
insights about network structures and have helped them to
communicate those insights to others (Freeman).

Two distinct forms of display have been used to construct images
of networks, one based on points and lines and the other on
matrices. In most point and line displays the points represent
social actors and the lines represent connections among the
actors. In matrix displays the rows and columns both represent
social actors and numbers or symbols in the cells show the social
connections linking those actors. The overwhelming majority of
network images have involved the use of points and lines. In the
present paper, therefore, I will focus on the development of
those point and line imagess.

There have been five fairly distinct phases in the development
and use of point and line displays in social network analysis.
First, beginning in the 1930s, graphic images were produced by
hand. They were ad hoc and their success varied with the insight
and artistic skill of their creator. Second, in the early 1950s,
investigators began to turn to the use of standard computational
procedures to produce images. Third, in the 1970s, computers
became widely available and began to be used to produce machine
drawn images automatically. Fourth, in the 1980s, the presence of
personal computers encouraged investigators to develop images
that could be displayed on monitors and in color. And fifth and
finally, in the 1990s, the availability of browsers and the World
Wide Web opened up all sorts of new possibilities for graphic
display. I will review this history and the present state of the
art in this paper.

2. Hand Drawn Images in Social Network Analysis

Network analysts study the patterning of the social connections
that link sets of actors. For the most part they seek to uncover
either or both of two kinds of patterns. They often look for
social groups -- collections of actors who are closely linked to
one another. Or, alternatively, they look for social positions --
sets of actors who are linked into the total social system in
similar ways.

From the beginning, network images have been used to call
attention to one or both of these two kinds of structural
patterning. Some images are constructed in such a way that they
emphasize important features of group structure. Other images
stress similarities and differences in the positions occupied by
actors. And some images manage to reveal a good deal about both
groups and positions at the same time.

The use of visual images to display these kinds of social
patterns stems, in large part, from the fact that images were
central features of the foundational work of Jacob L. Moreno
(1932, 1934). In discussing his approach, Moreno (1953, pp.
95-96) stressed his reliance on the use of pictorial
representations to map social linkage patterns:

We have first to visualize . . . A process of charting has been
devised by the sociometrists, the sociogram, which is more than
merely a method of presentation. It is first of all a method of
exploration. It makes possible the exploration of sociometric
facts. The proper placement of every individual and of all
interrelations of individuals can be shown on a sociogram. It is
at present the only available scheme which makes structural
analysis of a community possible.
Moreno began by defining a collection of actors and some relation
that might or might not link any particular pair of those actors.
He then collected data on who was linked to whom and used those
data to construct a picture that displayed the patterning of
linkages. In his earliest images Moreno used points to represent
actors. And when the relation linked any pair of actors, he drew
a line connecting the points representing the two. This kind of
drawing is shown in Figure 1.




Figure 1. Image of a Pattern of Linkages (Moreno, 1932, p. 101)

 Figure 2. Moreno's Image of Who Recognized Whom Among a
Collection of Babies (Moreno, 1934, p. 32).



Technically, the object shown in Figure 1 is a graph. A graph
consists of a set of points along with a set of lines connecting
pairs of points. Moreno was, so far as I can discover, the first
to propose using that kind of image to reveal important
structural information about social linkage patterns. In the
drawing of Figure 1, he suggested that the individuals at the top
and the bottom were "dominant" and that they were linked both
"directly" and "indirectly."

Very soon Moreno (1934) generalized this approach. He suggested
that there might be a directed relation linking two actors A and
B. Suppose, for example, that A reports responding to B while B
does not respond to A. In such cases Moreno suggested drawing a
directed line -- one with an arrowhead -- from the point
representing A to the one representing B. Then if each responded
to the other (see C and E in Figure 2), he proposed drawing a
line without arrowheads (but with a small crossing line splitting
the line connecting the pair).


This kind of drawing is called a directed graph. It reveals
important structural features of the data. Baby A for example was
seemingly at the bottom of some sort of hierarchy of recognition;
it was not recognized by any of the others. In contrast, B, D, G
and H were at the top; each was recognized by two others. And C,
E, F and I were in the middle; each was recognized by one other.
But the hierarchy is far from strict; C and E recognized each
other, and G, H and I formed a cycle in which G recognized I, I
recognized H and H recognized G.

It should also be noted that there are no crossing lines either
in Figure 1 or Figure 2. Later, Moreno (1953, p.141) specified
this idea as a general rule for constructing sociograms. He said,
"The fewer the number of lines crossing, the better the
sociogram." This rule is still widely used.

Moreno did not limit his drawings to these simple forms. He used
variations in both the locations and the shapes of points to
convey additional information about the structural properties of
social networks. Consider, for example, the image shown in Figure
3. The data are friendship choices made by fourth grade children.
Boys are shown as triangles and girls as circles. All the boys,
moreover, are drawn on the left of the image and all the girls
are on the right. The different shapes and the placement of
points show dramatically the segregation of choices by gender. In
fact, only one boy chose a girl and no girl chose any boy as a
friend.

Moreno often collected data both on who liked whom and who
disliked whom. He combined both liking and disliking in a single
multigraph in which he used color to show both relations in the
same picture. Figure 4, for example, shows his image of positive
and negative choices in thirteen members of a football team,
including two substitute backs. There, the points are located
more or less in the positions the individual players occupy on
the field. The red lines depict positive choices and the black
lines are negative choices. Moreno noted that 5RB, the fullback,
received no positive choices and was the target of four negative
ones. As he put it (1934, p. 213), "when (he) is running with the
ball he is not apt to get the maximum of cooperation in
interference and blocking."

Moreno used all sorts of arrangements of points in order to
stress particular important features of inter-actor links. But,
when he had no specific basis for arranging points in one
particular form or another, he simply arranged them into a
circle. Figure 5 shows his image of positive links among the
members of a school fraternity. In that picture, actor 5/11, the
president of the fraternity, is the point chosen most often.


Figure 3. Friendship Choices Among Fourth Graders (from Moreno,
1934, p. 38).

 Figure 4. Positive and Negative Choices in a Football Team
(Moreno, 1934, p. 213).

 Figure 5. Positive Choices in a School Fraternity (Moreno, 1934,
p.51).



Thus, in his early works, Moreno introduced five important ideas
about the proper construction of images of social networks: (1)
he drew graphs, (2) he drew directed graphs, (3) he used colors
to draw multigraphs, (4) he varied the shapes of points to
communicate characteristics of social actors, and (5) he showed
that variations in the locations of points could be used to
stress important structural features of the data.

Except for his use of color, other investigators were quick to
follow Moreno's lead. The expense involved in using colored ink
in printing led publishers to refuse to accept colored images.
Nevertheless, black and white pictures of both graphs and
directed graphs began to appear everywhere.

Many early images adopted Moreno's practice of arranging points
into a circle. In fact, structural analysts keep returning to the
circle as a way of arranging their points (Loomis, Morales,
Clifford and Leonard, 1953; Lesniak, Yates, Goldhaber and
Richards, 1977; Krackardt, Blythe and McGrath; Mitchell, 1994;
Kirke, 1996).

A good many investigators have followed up on Moreno's practice
of using variations in the shapes of points to call attention to
differences in actors' characteristics. In Figure 12 (shown
below), for example, Northway (1940) reported the use of line
drawings of children's faces to distinguish boys from girls. And
in Figure 14, Bock and Husain (1952) used the standard symbols
for male and female to make the same distinction.

To this day, investigators continue to innovate by producing new
ways to use variation in the shapes of points to communicate
important structural features of their data. An elaborate display
was employed in a recent picture produced by Baird, D. J.
Luczkovich, and R. R. Christian. (1998). They studied the food
web in the St. Marks National Wildlife Refuge, Apalachee Bay,
Florida and constructed an image of who eats whom in that
community. Their picture is shown in Figure 6. They described the
structural pattern in these words:

The producer base of this sub-web shows shoal grass (Halodule
wrightii); primary consumers shown are various species of
herbivores and detritivores including meiofauna (represented here
by a harpacticoid copepod and a nematode), amphipods, and
polychaetes; secondary consumers shown are juvenile spot
(Leiostomus xanthurus) and pinfish (Lagodon rhomboides); tertiary
consumer shown is the gulf flounder (Paralichthys albigutta).
Although a good many investigators innovated in introducing new
point shapes, a far more common concern has been with the
question of how to locate the points on the page. Moreno's idea
(shown in Figure 4) of placing points in positions that map to
their actual locations in physical space has been widely used,
particularly by rural sociologists and by geographers.

In their study El Cerrito in rural New Mexico, for example, the
sociologists Leonard and Loomis (1941) drew an image of various
kinds of visiting patterns as an overlay on a map of physical
sites occupied by families. It is shown in Figure 7.


Figure 6. A Nautical Food Web.

 Figure 7. Visiting Patterns among Households.

 Figure 8A. A Map of Russian River Trade Routes.

 Figure 8B. A Graph of Russian River Trade Routes.



Festinger, Schachter and Back (1950) did the same thing when they
overlayed network connections over a map of residence locations.
But the geographer, Pitts (1979), converted an actual map of
twelfth and thirteenth century Russian river-based trade routes
among hamlets into a social network. His map and his network are
both shown in Figure 8. In an analysis of the data embodied in
this figure Pitts discovered that among all these hamlets, the
one with the highest centrality was the one named Moscow.

Quite early in this process, investigators began to introduce
innovations designed to extend Moreno's general approach in ways
that would emphasize structural features of special interest to
them. One structural feature that was of great interest to early
investigators was the sociometric status of each point. The
sociometric status of a point was defined as the number of
choices, or the strength of the choices, received by that point.
Investigators thought that this might be a good index of power or
influence.

Lundberg and Steele (1938), for example, specified the nuclei of
a network as those actors with high sociometric status. They
constructed their famous "lady bountiful" image by making the
nuclei larger than other points and by placing them near the
middle of their drawing. Then they arranged the points
representing actors who were chosen less often in circles around
the nuclei. See Figure 9 where the number associated with a point
is based on an index of the social class of the actor represented
by that point.


Figure 9. The "Lady Bountiful."

 Figure 10. Apparatus for the Display of the Data of Figure 9.

 Figure 11. A Target Sociogram.



An interesting footnote to this figure is that Lundberg's
father-in-law, Chapin (1950), developed a very early
three-dimensional display of social network data. He began with
the data from the lady bountiful diagram and built an apparatus
that showed the social status, the outdegree and the indegree of
every point. In Figure 10 the vertical dimension is social
status, the horizontal one is outdegree and the one running in
and out is indegree. Chapin argued that the center points of
Figure 9 are really isolates and should be displayed as such.

In any case, later investigators picked up on Lundberg and
Steele's idea of varying the sizes of points in order to
emphasize their differences in sociometric status. Loomis (1946)
used it extensively, to stress the differential statuses of the
families he studied. And recent images produced by Krempel (see
Figure 28 below) still vary point sizes to call attention to
their sociometric statuses.

Northway (1940) was also influenced by the Lundberg-Steele
approach. She generalized their idea of locating popular points
in the center of network diagrams. She used that idea to develop
a systematic procedure for calling attention to differing levels
of sociometric status. She called her display a target sociogram.
Consider a nested series of concentric circles, like the ones
shown in Figure 11. Each circle contains points that are equal in
terms of how frequently they were chosen. Points in the central
circle are more central in the sense that they were chosen more
often. Points at the edge were chosen less often. The lines
connecting them represent the primary links between pairs. And
all the points are placed in the rings in such a way that the
lines connecting them are relatively short. Like Moreno's dictum
about lines not crossing, Northway's rule that lines should be
short has been widely adopted.

Grant, one of Northway's students (Northway, 1952) drew that
target sociogram shown in Figure 12. In her drawing, faces are
used to designate males and females. And in addition, Grant
shaded her drawing in such a way as to emphasize its center and
thereby to call special attention to the more popular actors.
And, as was the case for point size variations recent images
produced by Krempel (see Figure 28 below) still highlight the
centers of images.


Figure 12. Grant's Drawing of a Target Sociogram of a First Grade
Class (from Northway, 1952).
 Figure 13. McKenzie's Target Sociogram Board (from Northway,
1952).


Clearly, the kind of arrangement shown in Figures 10 and 11
emphasizes the sociometric status of points. Since status was a
major focus of interest in that period, Northway's procedure
caught on very quickly. Its success led one of Northway's
colleagues, McKenzie, to design and construct a different kind of
apparatus, one designed for displaying network structure easily
(Northway, 1952). She called it the target sociogram board. It is
shown in Figure 13. It was built from a round piece of pegboard
upon which circles were painted. Points were pegs that could be
plugged in anywhere on the board. Simply attaching a rubber band
to the two points could show a mutual link between a pair of
points in question. Thus the board allowed for the construction
of quick and simple displays emphasizing certain structural
features of a data set.

Most early network drawings followed one or another of the image
designs introduced by Moreno, Lundberg and Steele or Northway.
But, beginning in the 1950s, some investigators began to seek
ways to use standard computational procedures as part of the
process of producing point and line drawings. Their efforts will
be reviewed in the next section.

3. Point and Line Images Grounded in Computation

The earliest use of computational procedures in producing point
and line diagrams all focused on the problem of determining
locations for the points. Bock and Husain (1952) and Proctor
(1953) were the first to report using computational procedures to
aid in placing points. They both used factor analysis, but
produced very different kinds of images.

Proctor was part of a study team of rural sociologists who
examined the social structure of the Turrialba Valley of Costa
Rica. The study included a questionnaire that was administered to
the heads of households in two communities. One of the questions
was "In case of a death in the family, whom would you notify
first?" The answers to that question were examined in detail.
Proctor counted the "agreements," the number of times two actors
chose one another and/or both chose the same third actor). The
matrix of agreements was converted into a matrix of correlations
and a factor analysis was performed on that matrix.

Proctor used the results of the factor analysis to partition the
families into groups. Each of the first seven factors was
strongly associated with a different subset of families. Each of
these seven factors was used to define a "clique" of families
that were linked together. Figure 14 was produced by arranging
the members of each "clique" into a circle and placing the seven
circles in arbitrary locations in the page.


Figure 14. "Cliques" of Families in Atirro, Costa Rica.
 Figure 15. Clusters of School Children (Bock and Husain, 1952).


Bock and Husain (1952) also used factor analysis. They asked each
of the 16 members of a ninth grade class to rank all of the
others in terms of their desirability as collaborators on a joint
research project. Thus they collected quantitative data on the
perceived strength of the ties connecting pairs of actors.

Like Proctor, Bock and Husain began by correlating the various
students' rankings and they performed a factor analysis of the
correlation matrix. But they used the results of their factor
analysis to construct a different kind of image. Their analysis
took advantage of the fact that factor analysis maps each
observed variable (in this case the 16 correlations) into a new
variable, or vector. These new variables are produced in such a
way that most of the variance in the original data is associated
with the first new vector, less with the second, still less with
the third and so on.

Considered in that way, the success of factor analysis rests on
the hope that the first few new vectors will account for almost
all of the important patterning of the original data. Bock and
Husain concluded that, for their data, the first two factors
captured the important patterning. These two factors then were
used to assign locations to points on a two-dimensional surface.
The placements are shown in Figure 15. Note that Figure 15
contains no lines, only points. Since the data were quantitative,
the kinds of lines used by Moreno and Northway for binary data
were not appropriate in this context. Instead, it is the actual
two-dimensional distances between pairs of points that tell the
story of who is to be grouped with whom.

Two structural features of the data are immediately obvious from
inspecting these distances. First there is a clean separation
between the boys and the girls. And second, the boys are more
spread out and the girls are arranged more compactly. The boys
seem to want to work with other boys and the girls with other
girls. And the girls have apparently formed a more close-knit
group when it comes to choosing work partners.

Without computers, the use of factor analysis in the early 1950s
was extremely cumbersome. It did, however, have a clear advantage
over earlier procedures. It employed a standard procedure and
therefore it did produce results that could be replicated;
different investigators, using the same data, would produce the
same image.

By the 1960s computers were generally available. That provided
the opportunity for the use of more elaborate computations.
Laumann and Guttman (1966) used a computationally demanding
procedure, multidimensional scaling, as a device for locating
points from a network analysis. And they were the first to
produce an image that was designed to give the appearance of
three dimensions.

Laumann and Guttman studied the links among 55 occupational
categories. They asked individuals to report their own
occupations and those of seven of their close associates. Then
they built a 55 by 55, occupation by occupation, matrix and
tabulated the frequencies in which a respondent's occupation was
paired with each of the others.

They then used multidimensional scaling to map the data matrix
into a three-dimensional array. Multidimensional scaling is like
factor analysis in that is a procedure for data reduction. The
investigator begins by specifying the number of dimensions
desired in a solution, say 1 or 2 or 3. Then the original data
are entered and the multidimensional scaling program finds the
best possible arrangement of points -- the one that come closest
to capturing patterning in the original data -- in the specified
number of dimensions.

The three-dimensional solution obtained by Laumann and Guttman is
shown in Figure 16. They used that image to define the 17
occupational clusters shown. In general, each of their clusters
contains occupations that have similar prestige levels. High
prestige occupations are displayed at the top of the diagram and
low prestige ones at the bottom.


Figure 16. Clusters of Occupations.

 Figure 17. Links between Corporations and Corporate Directors.

 Figure 18. Individual and Corporate Proximities (Levine, 1979).



Thirteen years later Levine (1979) introduced a tool that could
be used to locate points in two mode network data. In two mode
data the columns and the rows of the data matrix refer to
different objects. And Levine's algorithm allows the user
simultaneously to assign locations to points of both kinds: those
associated with the rows of a data matrix and those associated
with the columns. This procedure has many names, but it is now
generally known as correspondence analysis.

To understand how correspondence analysis works, consider the
data used by Levine. Figure 17 shows his data on ten individuals
each of whom served on the board of directors of at least two of
seven large corporations. His aim was to display which
corporations were similar in terms of sharing directors, which
directors were similar in terms of sharing corporate boards and
which individuals were associated with which corporations, all at
the same time.

All of these properties are displayed in the pattern of
proximities of Figure 18. Note particularly that the two banks
are widely separated, as are the two automobile manufacturers.
Moreover, each of the manufacturers is associated with a bank.
This is exactly the kind of patterning described by Freeman
(1968).

The image in Figure 18 was still drawn by hand. But the practice
of programming computers to draw point and line images of
networks actually began a bit earlier -- in the early 1970s. This
development will be reviewed in the next section.

4. Computer Generated Point and Line Images

In the 1970s Alba (1972) worked with Gutmann and Kadushin. They
developed an early program SOCK that, along with a
Stromberg-Datagraphics 4060 plotter, could produce point and line
graphics automatically. The program was intended to serve as a
general-purpose network analysis and image-producing device. It
was used to produce the illustrations used in Kadushin's (1974)
book on intellectual elites. Figure 19 is from that book. It
shows an image of reported contacts among American intellectuals.

As they were in the image in Figure 16, points here were located
by using multidimensional scaling. But, beyond the
machine-produced picture, another important innovation was made
in this work. The program used one set of calculations to locate
the points on the page and it used a second independent set of
calculations to designate subgroup memberships. Then subgroups
were designated by drawing lines to enclose their members.

When subgroups designated in this way are relatively compact,
each of the calculations validates the other. This same kind of
approach is now common. Though they used different models for
placing the points and for specifying the subgroups, both Webster
(1993) and Frank and Yasumoto (1996) produced images that placed
points on the page and used an independent computation to specify
subgroups.

For the next several years, most programming efforts went into
analysis and little attention was devoted to getting computers to
draw network graphics (Freeman, 1988). But, in 1978 Lesniak,
Yates, Goldhaber, and Richards (1978) described a program that
could take output from Richards' network analysis program,
NEGOPY, and produce graphic images.

That same year Klovdahl reported adapting ORTEP, an early program
designed to produce images of molecules, to the production of
network images. Four years later he (1982) published an
ORTEP-produced picture. It is shown in Figure 20.


Figure 19. Alba and Kadushin's Image of Contacts among
Intellectual Elites.

 Figure 20. ORTEP Rendition of the Data of Figures 9 and 10.

 Figure 21. Images of Social Links in Canberra Australia.



The image shown in Figure 20 is based on Chapin's
three-dimensional rendering of the "lady bountiful" data. In 1950
Chapin had to build a physical apparatus to display the image,
but 28 years later Klovdahl was able to use a computer to draw a
replica of that apparatus in which the perspective cues support
the illusion of three dimensions.

Klovdahl then went on to develop View_Net, a program designed to
draw elegant images on an Evans & Sutherland real-time graphics
workstation. He later moved the effort to a Silicon Graphics work
station and developed View_Net II. View_Net II is designed to
work with a database and to facilitate the integration of
analysis and graphic imaging. It was used to produce the image
shown in Figure 21. That figure shows the connections among some
of the individuals studied by Klovdahl (1989) in Canberra,
Australia. It yields a very convincing three-dimensional illusion
and, in that way, it anticipates much of the newer work described
below.

All these early machine generated images produced output on
plotters. It was not until the late 1980s that the introduction
of screen oriented personal computers reduced the reliance on
plotters. The production of screen images permitted much more
flexibility and, for the first time since Moreno's work in the
1930s, facilitated the use of colors in pictures of networks.

5. Screen Oriented Point and Line Images

I will review six screen-oriented programs here. Two are special
purpose programs, programs that do only one job. The other four
are designed as general-purpose network graphics packages; they
will produce many kinds of images. I will begin with the special
purpose pair.

Duquenne (1993) wrote a program called GLAD that is designed to
organize network data into a Galois lattice (Freeman and White,
1993). Like correspondence analysis, a Galois lattice is designed
to deal with two mode data. But a Galois lattice embodies a
completely different approach and it produces a different
arrangement of points. It displays an order structure, one in
which the dependencies among the row objects, the dependencies
among the column objects and those between the two are
simultaneously revealed.

Figure 22 displays the Galois lattice arrangement of the data on
interlocking directors from Figure 17. It provides a different
perspective on corporate structure. First, it should be noted
that two sets of directors (Jamieson and Kappel on one hand and
Mortimer and Oelman on the other) form structurally equivalent
pairs; they are substitutable. But more important, the lattice
demonstrates the competition between the two banks; they have no
common directors. In addition, it shows that the board member,
Houghton, is in some sense less central than Williams: Houghton
never serves on a board of which Williams is not a member.
Similarly, the Ford Motor Company falls below the First National
Bank; Ford has no shared directors with any corporation except
First National while First National does share directors with
firms other than Ford. It is also clear that most firms and most
directors are in the First National or the Chase camp, but not
both. Learson is the only director that does not serve on either
bank board, and Connor is the only one who serves on one bank
board as well as on the board of a company (I.B.M.) that is
associated with the other bank. Finally General Electric is the
only company that shares a director with both banks. All this
reveals a great deal about this corporate structure that is not
necessarily apparent in output of the correspondence analysis
shown in Figure 18.


Figure 22. The Data of Figure 17 Displayed as a Galois Lattice.
 Figure 23. Dynamics of the Freeman and Freeman Data.
To activate image, click thumbnail
then click figure.
 Figure 24. A Rendition of the Image from Figure 23 Using
Multicolor.
To activate image, click thumbnail
then click figure.


I introduced another special purpose screen oriented program to
network analysis in a web publication 1997. It is called Moviemol
and it uses animation to display dynamic processes. Moviemol runs
on PCs (but only under DOS, not Windows), on the IBM-RISC 600 and
on any Silicon Graphics work station. Moreover, like many
molecular modeling programs, Moviemol is difficult to use in
social network analysis. The problem is that the program simply
knows too much chemistry. It knows which pairs of points should
be connected by lines and which pairs should not. Overriding this
knowledge is very difficult. The program displays network data
best when all lines are suppressed and the structure is displayed
simply by the distances among points.

I can illustrate its use with a data set collected by Freeman and
Freeman (1980). The data were collected in the context of an
experimental computer conference conducted in the late 1970's.
The participants were all separately engaged in research in
social networks. They were linked to a common computer that was
programmed to facilitate communication through two structures,
listservers and an e-mail message facility.

Before the computer connection was started, the participants were
asked to characterize each other into one of four classes: 1)
close personal friend, 2) friend, 3) acquaintance, 4) someone
they had never heard of. Then, after eight months of access to
each other via a computer-based communication facility, everyone
was questioned again.

Figure 23 shows a dynamic image of the changing patterns through
8 months of computer linkage. After studying the animation of
this transition, I was struck by the fact that, based on their
patterns of movement, there were four subsets of individuals. I
assigned different colors to the members of different subsets.
The result is shown in Figure 24, which is simply a replay of
Figure 23 with the colors added.

The four subsets turned out to have distinct identities that were
consistent with the development of social network analysis. Those
who moved up and to the left are the people who became the core
of the emerging interdisciplinary field of social network
analysis. Those who moved into a cluster at a point in the lower
right of the picture are sociologists who viewed network analysis
as a small part of sociology and came together to defend the
discipline. The two who didn't move did not participate in the
computer experiment at all. And the three that moved away at the
bottom are three who left the experiment and the network paradigm
entirely. Thus, the process of examining the animation suggested
looking for this split and the split helped to understand the
data.

The four general purpose network display programs that will be
reviewed here are Krackplot, Pajek, NetVis and MultiNet. They
were all developed at about the same time, in the 1990s. In 1995
Krackhardt, Blythe and McGrath (1995) released a completely
screen oriented version Krackplot to the public. That program
runs in DOS on a PC. It produces screen images and it can
generate good quality printed output. Various algorithms for
locating points are included, as are various devices for moving
and editing points and changing their shapes and colors.

To illustrate the potential of Krackplot, I will use a picture
that was published by Mitchell (1994). Mitchell arranged the
points into a circle and drew in all the strong and the weak ties
surrounding a homeless woman (Respondent). His aim was to stress
the fact that the data displayed a structural form in which the
actors were broken up into three fairly distinct and socially
meaningful subgroups. But, given the circular arrangement of
points, the subgroup structure is not immediately apparent.


Figure 25. Mitchell's Image of the Social Support Network of a
Homeless Woman.

 Figure 26. A Krackplot Rendition of the Data of Figure 25.



Consider instead the image of the same data shown in Figure 26.
It was produced using Krackplot's standard routines for locating,
labeling and coloring points. A spring embedder assigned point
locations. Spring embedders are based on the notion that the
points may be thought of as pushing and pulling on one another.
Two points that represent actors who are close will pull on each
other, while those who are distant will push one another apart.
Several algorithms have been developed that weight these pushes
and pulls in different ways. But they all seek to find a global
optimum in which there is minimum stress on the springs
connecting the whole set of points.

In Krackplot the user assigns shapes and colors. In this case,
the respondent, her friends at the shelter and her social worker
are drawn as rectangles. The respondent's rectangle is black and
her friends' rectangles are green. The members of her family are
shown as diamond shaped and blue. And the members of her
husband's family are shown as oval and red.

All these features, especially the locations assigned by the
spring embedder make it clear that there are three tightly knit
subgroups here. They are bridged by only a few ties, and all but
one of the bridging ties involve the respondent.

In 1994, at about the time Krackplot was released, Batagelj and
Mrvar released DRAW and ENERG, the first in their series of
network drawing programs. This effort culminated in 1996 in the
release of the general network analysis and drawing program Pajek
(Spider). Pajek includes several algorithms for locating points
and it permits the user to move points and to change labels,
shapes and colors. Pajek, moreover, can handle really huge data
sets and it can locate points in either two or three dimensions.
A weakness of its three-dimensional display results from the fact
that it produces too few perspective cues to be very convincing
as a medium for three-dimensional displays.

Figure 27 shows an image produced by Pajek. It is based on the
same data that were used to produce Figures 25 and 26. Although
variations in the colors and shapes of points are not used here,
they are not necessary, since the tripartite structure of the
data is very clearly evident.

At about the same time, Krempel also developed a general graph
drawing program, NetVis. NetVis runs only on a Silicon Graphics
workstation. It can locate points -- in two dimensions -- with
any of several versions of the spring embedder and it permits the
user to modify those locations. Points may vary in their sizes,
shapes and colors.

A particularly notable feature of Krempel's program is that it
includes a procedure that permits the use of a spring embedder to
locate points in two mode data. Like correspondence analysis,
this permits the investigator to represent proximities in the
data from both rows and columns at the same time. To illustrate,
Krempel used data from Davis, Gardner and Gardner (1941) on the
attendance of 18 women in 14 informal social events. In his
embedder, two events are placed in close proximity if they share
many common attendees. Two women are close if they attended many
events together. And a woman is close to an event if she attended
that event. The result of Krempel's spring embedding is shown in
Figure 26. The figure makes it easy to see that the women form
two groups and that the two groups are bridged by a few events
(E7, E8, E9) that brought members of both together.


Figure 27. A Pajek Rendition of the Data of Figure 25.

 Figure 28. Two Mode Data on Women's' Attendance at Social Events
(Data from Davis, Gardner and Gardner, 1940).
 Figure 29. A Three Dimensional View of the Brajkovich Data.

Get FREE 3D glasses! Email your
postal address to David Krackhardt,
krack@andrew.cmu.edu.


And, still at about the same time, Richards and Seary produced
MultiNet, another general program for network analysis and
drawing. It can locate points using correspondence analysis or
any of several variations of that procedure. It produces two or
three-dimensional images and permits users to rotate the images
and to color points. A major graphic innovation in MultiNet is
that it allows the use of red/green anaglyph glasses to produce
the full illusion of three dimensions.

To illustrate, I will use a data set described by Brajkovich
(1994). Brajkovich collected data on 15 workers in a small
high-tech start-up company. The company included 6
business/management types, 6 engineers, and 3 technicians. Each
was asked to report who worked closely with whom. The data
reflect the general consensus of all of the workers.

The data were analyzed using a procedure similar to factor
analysis based on eigenvectors. The first three dimensions were
used to construct the images shown in Figure 29. The smaller
image in the upper left corner is a regular two-dimensional
projection of the output. It identifies the occupation of each
actor. The business types are green, engineers are red and the
technicians are blue. That image shows that the managers are
clustered together and pretty well segregated from the engineers.
The engineers also cluster, and they buffer the technicians from
the managers. The technicians do not cluster together.

The larger image on the right reveals the third dimension. If you
look at it with glasses in which the left eye is red and the
right eye is blue or green, it produces an amazing 3D effect.
Points 1, 6 and 11 leap out at you, and points 5, 9 and 13 recede
to the back. From this, we can see that the structure is more
complicated than it appeared in the two-dimensional image on the
left.

Overall, then, these general-purpose programs provide
investigators with a wide range of tools for exploring the
structural properties of network data. They involve a number of
alternative algorithms for arranging points. They facilitate
moving points and changing their shapes, their colors and their
labels by the investigator.

But these programs are still limited in at least one important
respect. Each of them can be used only on one, or a few,
particular computers and/or operating systems. This means that
they are less than universally available. Anyone who produces an
image can mail or email it to others. But the receivers can only
inspect it; they cannot themselves manipulate the image in the
hope of learning more or of developing a new insight.

Indeed, it was not until the 1990s that the World Wide Web
provided an opportunity for the kind of flexibility where viewers
can actually interact with the images they receive. Web browsers
permit the production of images that can not only be viewed, but
also can be manipulated and explored by viewers, regardless of
the machine on which they are working. Three of these more
flexible tools will be reviewed in the next section.

6. Network Images in the Era of Web Browsers

The advent of the World Wide Web in the mid-1990s revolutionized
opportunities for network imaging. Both GIF and JPEG images will
be displayed automatically by almost any browser. But many newer
programs intended for browser application are somewhat less than
universally adaptable. Some browsers will automatically support a
given display program, while others will not. And in some cases a
browser will be able to support a display program, but it will
require that a "helper" application be downloaded before an image
can be displayed.

For these newer display programs, therefore, I will present
figures in pairs. In each case, the first of a pair will present
the image in a form that is more difficult to display. The second
will approximate the first in the form of a PowerPoint slide
show. I encourage viewers to use the first of each pair whenever
they can. The second will always provide a less dramatic
collection of images.

Program developers working in social networks have been
relatively slow to adapt to this revolution. But a student at the
University of Illinois, Michael Chan, has produced a Java program
that was designed specifically for application to the analysis
and display of social networks. His program is a weighted spring
embedder. It takes as input data that record the strength of the
edge connecting each pair of points. It uses these strengths to
assign optimal lengths to hypothetical springs linking each pair.
Then it seeks to find a way of locating all the points such that
the overall tension on the collection of springs is minimal.

To illustrate Chan's program, I will return to the Davis, Gardner
and Gardner data shown in Figure 28. Here the data have been
converted to a one mode (woman by woman) form in which each cell
contains a record of the number of events co-attended by the row
woman and the column woman. These numbers, then, provide an index
of the strength of the connection between each pair of women.
They are the input for the Java program displayed in Figure 30A.
That program permits the viewer to arrange the 18 women randomly,
in a circle or using Chan's spring embedder to seek an optimum
display. If your browser will not display Figure 30A, you might
want to download a newer browser, or you can see some of the same
images by looking at Figure 30B.


Figure 30A. JAVA Image of Spring Embedding the Data Displayed in
Figure 28.
 Figure 30B. PowerPoint Slide Show (selected images).


A second browser-based way of handling images is provided by VRML
(Virtual Reality Modeling Language). Though VRML is probably not
as ubiquitous as Java, VRML files are handled automatically by
most recent browsers. And if your browser lacks the ability to
interpret VRML, it is relatively simple to download a free
interpreter and attach it to your browser. After that, any VRML
file can be displayed on your computer.

VRML provides elegant visual images and a convincing illusion of
three dimensions. It allows the viewer a wide range of ways to
manipulate the image. You can expand or shrink it. You can rotate
it in any direction, pan around it, tilt it or slide it in any
direction. You can, moreover, move away from the image or even
move into it and right on through it. It takes a while to get
used to VRML controls, but once they are mastered, VRML provides
great flexibility.

A VRML image of data collected by Webster (1994) is shown in
Figure 31A. This image is a three-dimensional representation of
data on friendship ties collected at a residential college at an
Australian university. All 217 residents were interviewed
individually and asked to name their friends within the college.
The residents were also asked to indicate the strength of each
friendship tie. The original matrix was symmetrized, then
analyzed using correspondence analysis. The first three axes of
the correspondence analysis were used to locate points in the
picture presented here.



If your browser will not display the image in Figure 31A, you can
download a helper application from this address, install it and
try again. Or you can see three snapshots from the VRML image in
Figure 31B.




Figure 31A. VRML Image of Webster's Australian Friendship Data.

 Figure 31B. PowerPoint Slide Show (selected images).



You will probably have to expand or shrink the image of Figure
31A to see the whole of it. You will notice, then, that it has an
interesting pattern. When it is rotated, it displays four wings
that are projected out and seem to be made up of individuals who
are not part of the central core. Webster set out to identify
these wings.

In a separate analysis, she uncovered 19 friendship groups by
extracting cliques and merging those that displayed major
overlaps. She used her ethnographic knowledge to name these
groups. The members of four of the 19 clique-clusters turned out
to anchor the four wings. They are displayed through the use of
colors assigned to the points in the figure.

The RELIGIOUS group shown in yellow is a devout group of
residents. They are bipolar with the mostly male GRUNGE group
(identifiable by their disheveled appearance) who are shown in
silver. The MATH HEADS are colored purple. They are bipolar with
a very social group (colored light blue) that Webster dubbed THE
WOMEN. The fact that there is agreement between the two
independent analyses, then, argues for the validity of both.

Finally, the third browser-based display program, MAGE, is
unlikely to be found already installed on your machine. But its
strengths are great enough that it is worth the effort of
downloading it from here and installing it as a helper
application. Once it is installed in your browser, MAGE will be
automatically mobilized whenever an appropriate image is loaded.

MAGE was originally designed by Richardson and Richardson (1992)
for viewing and manipulating the images of protein structures. It
is a very small program. But it is flexible and it is
exceptionally easy to use. It simply draws images and does not
impose any restrictions based on its knowledge of chemistry. It
requires input in a simple format, called kin. At a minimum, a
kin file requires a three-dimensional list specifying x, y and z
locations for each point.

The creator of an image can draw points and lines, use color and
present the viewer with any number of preset views. But the
viewer has options for reading and saving files, changing
pictures, changing viewpoints and the like. In addition there are
three scroll bars on the right side of the screen. With them, the
viewer can control the zoom factor, depth clipping of the image
and the display of layers of complex images.

Once you have installed MAGE you will be able to view the image
shown in Figure 32A. The figure will show a series of images that
were part of a computer-based demonstration of MAGE by Freeman,
Webster and Kirke (1998). You can move through the series by
clicking on the box marked "ANIMATE" on the right of the screen.
Note also that by moving the cursor in the screen you can explore
different views of the image on the screen.

Kirke (1996) collected data on friendship ties among teenagers in
a Dublin suburb. In the first slide, points were arranged using
multidimensional scaling of the original data as reported by
Kirke. The remarkable feature of this arrangement is the long
cycle running completely around the circle of points. But the
presence of that cycle turned out to be the result of the fact
that the data included a combination of two distinct relations,
friendship and sibling ties.

In the second image only the sibling ties are shown (in blue).
The third image, then, shows only the friendship ties (in gold)
and demonstrates that the cycle is absent. The fourth slide
distinguishes between males (in green) and females (in red) and
it shows both the (blue) sibling ties and the (gold) friendship
ones. Finally, the fifth slide shows the results of a new
multidimensional scaling -- this time restricted to just the
friendship ties.

This fifth slide shows many interesting structural features of
the data. The males form complicated clique-like structures. The
females form trees, without cycles. Any male can reach any other
male through an all-male chain. Members of the two collections of
females must rely on males to be linked.


Figure 32A. MAGE Image of Kirke's Data on Friendship among Dublin
Teenagers.

 Figure 32B. PowerPoint Slide Show (selected images).



7. Conclusions

Overall, this review of the use of graphic imaging in social
network analysis points up a number of interesting trends.
Perhaps the most central is the clear indication that imagery
has, and has always had, a key role in network research. From the
beginning images of networks have been used both to develop
structural insights and to communicate those insights to others.

Most early images were constructed by the application of ad hoc
rules. As time passed, however, images have increasingly been
constructed by applying standardized procedures for placing
points in first two, and later in three-dimensional space. Early
applications used factor analysis. More recently, factor analysis
has been replaced by various forms of scaling, particularly
principal components and correspondence analysis. And, just in
the last few years, various forms of spring embedders are
increasingly used to place points.

Computers have been used to do the actual drawing for a number of
years. But, more recently, the network research community has
shown a tendency to construct and share screen-based images
instead of relying entirely on the production and distribution of
printed pages. This new approach facilitates the use of color and
animation. Currently it offers enough flexibility to allow
viewers to begin to interact with the images they receive.

Network analysts have always been able to learn by generating and
sharing visual images. Modern technology promises to enhance our
ability to learn from images by continuing to provide new tools
that allow for more powerful visualizations. Electronic journals
like the Journal of Social Structure provide a case in point. It
is obvious that a great many of the images provided in the
present JOSS article could not have been included in a standard
print journal. We are entering a new era in which we can use
color, animation and three-dimensional imagery to explore social
structural arrangements.

Future developments will undoubtedly extend current trends.
Network analysts already have made considerable progress in
developing programs for computation (Freeman, 1988). And, as I
have shown in this paper, we have made progress in developing
programs for visualization. We can look forward to similar
progress in developing database programs designed to facilitate
the storage and retrieval of social network data. But the real
breakthrough will occur when we develop a single program that can
integrate these three kinds of tools into a single program. Only
then will we be able to access network data sets and both compute
and visualize their structural properties quickly and easily.

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__________________________________________________________________
Walter Logeman  walter@psybernet.co.nz  http://www.psybernet.co.nz

____________________________________________________________
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Date:         Sat, 12 Feb 2000 16:58:44 +1300
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Comments: To: Group-L Noosphere Probes <group-l@lists.groupsense.co.nz>,
          josh@zesty.com, bonaanna@hotmail.com
In-Reply-To:  <LYR3-5577-2000.02.10-11.22.16--walter#psybernet.co.nz@lists.groupsense.co.nz>
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Barry, Brian, Dan, Josh, Bonna, all in Psyber-L,

Re: http://www.heinz.cmu.edu/project/INSNA/joss/vsn.html.

 "Visualizing Social Networks" by Linton C. Freeman of the
University of California, Irvine."


I have now read the paper, and I loved reading it.  I have lots
on my mind about it.

The article is a gem for anyone interested in graphical
visualisation, ie *presentation* of social networks.  Useful for
sociometry, though not sociometry.

Josh, I thought your comments are right on... thought you might
like a chunk of stuff i wrote on the theme.  I hope it is ok if I
quote here what you wrote as well... all that appended below.  I
agree with what you say there.

Without the maximum voluntary particpation of the participants
the "actors" as they are called in the article the representation
is not meaningfully accurate.  It is not just that it is not
ethical, it does not lead to action easily, at least not to
action by the participants.  It could be "market analysis".  It
is dead data, no matter how well presented.


The guts of it all is that if we are to have *great technology*
then the greatest of it all has to be to *get the conversations
going*! To call that a technology or a technique or even a "human
resource" is all wrong too... it is a skill and an art, and hard
to do!  Worth getting good at.

I own to being positively influenced by the Cluetrain ideas here
http://www.cluetrain.com/ though i have been saying slightly
derogatory things about the buzz aspect.

~~~

The gut of the guts is that sociometry reveals what is more
commonly called the Psyche.  "Visualizing Social Networks" is
imaging, imagining, symbol formation, abstraction... and when it
is done as *true sociometry* (see Moreno stuff below) then it
reveals the underlying unconscious depths of the group and its
culture ... Psyche.

~~~

One more thought on this... for those who are familiar with
HipBone Games:  http://home.earthlink.net/~hipbone/

In a HipBone Game we work from a social network, the players,
into a visual representation, the music of the ideas, and how
they interrelate.  The form of that image, is given. The group is
using an existing sociogram, voluntarily and deliberately to
reveal underlying aspects of the group life.

It could be seen that the given format of the board might skew
the data... I tend to think that it forces the players into a
deeper level of their interconnection.

I think of it as a sophisticated "continuum", in workshops, a
line with extremes at each end, and people place themselves on
the line.  More is revealed if people stick to the format of the
line, and jostle for their place and not cop out by being off the
line or sharing a spot with someone else.


Walter




~~~

Josh's comment on the Freeman article:

| It does justice to Moreno's contribution to the field which is
| rare, although I think misses some of his more profound and
| important observations.  There are two main spheres of influence
| that Moreno has had, one in the field of network analysis and the
| other in group psychotherapy. (He pretty much founded both, but
| because of his original habit of writing anonomously he is not
| often credited with either.) In this article the whole motivation
| for studying groups and the key notions of being warmed up, the
| participent observation, and the attempt to break down the
| concepts of objectivity and subjectivity are completely ignored.
| Yet in my opinion the graphic representations mean nothing
| without them...
|
| Still it is an amazing article... actually I am still reading it,
| but so far it is great...
|
| Josh

~~~

Below is some writing I did a couple of years ago.  Some were
discarded as it was not relevant at the time, but I think it is
relevant to the visualisation of networks discussion.


Sociometric method

A summation of some of:

"Moreno, J. L., 1951, Sociometry, Experimental Method and the
Science of Society. An Approach to a New Political Orientation.
Beacon House, Beacon, New York."


Moreno was critical of applying scientific methods used in
physics and biology to human beings. He devised approaches that
were radically opposed to these methods.  He pioneered methods
that go beyond "participant observation" because in sociometric
work there is the added dimension of the subject participating in
observation of his or her own life. (Moreno, 1951, p.17). This
shift has still not been recognised in the "social sciences". In
fact Moreno refused to call the other methods scientific when
they applied to humans. Using the methodology of physical science
when it comes to people does not work. In Moreno's words:
"Because of the value which the experimental method has shown in
these areas [physical and biological sciences] the conclusion has
been drawn by many writers that it can be applied to the social
sciences. But their optimism is unjustified. Mill's skepticism
was correct in principle; but he did not realise that it was the
experimental method which was at fault, and not the
inaccessibility and fleeting inconsistency of the social
phenomena.


"The chief methodological task of sociometry has been the
revision of the experimental method so that it can be applied
effectively to social phenomena."
(Moreno, 1951, p.31)


Moreno called his method sociometry, and devised criteria for its
application. He devised various methods to assist people to
spontaneously enter into collaborative sociometric research.
Psychodrama is the best known of these and has become a method of
group psychotherapy in its own right. However, it is only one
method of sociometric experimentation in Moreno's larger vision.


"In many forms of psychodramatic production, also, the
artificiality of the contrived experiment is overcome, the
experiment in situ and the life setting are one and the same
thing. Early types of psychodramatic procedure were experiments
in situ. The transfer of the psychodrama to a "theatre", a
laboratory, or a treatment room was a secondary and later
development."
(Moreno, 1951, p.34)


Moreno encourages a situation where there is both a cohesive
group and where individuals are fully in tune with their own
concerns.

Moreno, in the same article, enumerated rules which make up the
sociometric method.  Note that they are worded in more general
terms than those which apply to psychodrama.  When these rules
are applied to the psychodrama they describe the sociometric
nature of working with one person as the protagonist.  I will
summarise them here:


1.  Rule of the warming-up process.
           This refers to the readiness or level of spontaneity
       of the participants.

2.  Rule of "coaction" of the researcher in the group.
           Every participant in the group is a "researcher",
       and the leader is also a group member.

3.  Rule of universal participation in action.
           Under this heading Moreno describes the value
       of experiential learning.

4.  Rule of dynamic difference in group structure.
    Peripheral versus central.
       This refers to the difference between the
       underlying themes and the manifest content.
       To quote Moreno:  "there is a deep discrepancy
       between the official and the secret behaviour
       of members". (p.39)
       Moreno advocates that before any "social
       program" (presumably a psychodramatic enactment
       in the case of a psychodrama group ) can be
       proposed, the  director has to "take into account
       the actual constitution of the group." (p.39)

5.  Rule of adequate motivation
       "Every participant should feel about the
       experiment that it is in his {or her} own cause
       . . . that it is an opportunity for him {or her}
       to become an active agent in matters concerning
       his {or her} life situation." (p.39)

6.  Rule of "gradual" inclusion of all extraneous
    criteria.
       Moreno speaks here of "the slow dialectic process
       of the sociometric experiment.", this is similar
       to the process described by Whitaker and
       Lieberman.


__________________________________________________________________
Walter Logeman  walter@psybernet.co.nz  http://www.psybernet.co.nz

____________________________________________________________
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=========================================================================
Date:         Sat, 19 Feb 2000 20:02:11 +1300
Reply-To:     Exploring the Psyche in Cyberspace <PSYBER-L@HOME.EASE.LSOFT.COM>
Sender:       Exploring the Psyche in Cyberspace <PSYBER-L@HOME.EASE.LSOFT.COM>
From:         Walter Logeman <walter@PSYBERNET.CO.NZ>
Organization: psybernet
Subject:      Re: Facilitating and participating
In-Reply-To:  <di2b4s0hp8nmhpb195a76mbmjlkh47rsfe@4ax.com>
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I wrote this post here:

  Walter Logeman <walter@PSYBERNET.CO.NZ>
  Facilitating and participating
  Thu, 2 Dec 1999 15:01:36 +1300

Since then I have edited it a bit and put it on the Psybernet web
site:

http://www.psybernet.co.nz/writing/posts/facpart.htm

The topic is on my mind, and am interested in some discussion on
that.

What sort of experiences have others had around this?


Walter




_____________________________________________________
Walter Logeman
Psychotherapist
Harley Chambers
PO Box 13 543
Christchurch NZ
Phone: (643) 377 1206
Fax: (643) 377 1207
walter@psybernet.co.nz

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=========================================================================
Date:         Wed, 23 Feb 2000 20:28:01 +1300
Reply-To:     Exploring the Psyche in Cyberspace <PSYBER-L@HOME.EASE.LSOFT.COM>
Sender:       Exploring the Psyche in Cyberspace <PSYBER-L@HOME.EASE.LSOFT.COM>
From:         Walter Logeman <walter@PSYBERNET.CO.NZ>
Organization: psybernet
Subject:      Weblog
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All,

I have been exploring a Weblog as a possible format for group
interaction.  From what I can gather it is a format for
collaboration and communication that has heaps of potential.
Perhaps the forerunner of things to come.

As part of my testing I set up a Psybernet Weblog... I have got
it more or less functional... the stuff I have posted is mostly
to test it, however it is "content of interest" to some extent.

I can give people editorial status, so if you are interested in
contributing *psyberlogical* stuff, taking that notion very
broadly, let me know, I'd like to push this medium a bit.

You can also subscribe and simply get emails from the site.  To
discuss however, it means going back to the site.

I have created a "story" there, the beginning of a discussion
topic, called "Psybernet Weblog Development" ... it might be good
to discuss all this further there.

http://psybernet.editthispage.com/

Looking forward to some collaborative logging.


Walter
__________________________________________________________________
Walter Logeman  walter@psybernet.co.nz  http://www.psybernet.co.nz

____________________________________________________________
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=========================================================================
Date:         Wed, 23 Feb 2000 14:13:56 EST
Reply-To:     Exploring the Psyche in Cyberspace <PSYBER-L@HOME.EASE.LSOFT.COM>
Sender:       Exploring the Psyche in Cyberspace <PSYBER-L@HOME.EASE.LSOFT.COM>
From:         Sheryl Garner <CMCPsych@AOL.COM>
Subject:      Re: Weblog
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In a message dated 02/23/00 1:36:18 AM Mountain Standard Time,
walter@PSYBERNET.CO.NZ writes:

> http://psybernet.editthispage.com/

Hi Walter,

I very briefly visited the site, but accessed it through the main Psybernet
website because I couldn't get there from the URL.

And I mean my visit was really brief... but it was intriguing enough to pull
me out of a kind of psyber-l apathy to post today.  My impression of the site
is almost hallucinogenic!  Entering into your stream of conscious thought,
like *being Walter Logeman*?!

I have membership-to-the-site questions, though.  Will you sell my name to a
spammer?  Will I start receiving invitations to enter other on-line psyche
sites?  Will my email box become full of subject titles such as "Free on-line
psyche 2MB reserved in your name?"  Will the word psyche somehow evolve into
the word psychic and then I would get a whole raft of other worldly type
spams too?

If not, I think I might like to enter the discussion site.

Sheryl  =:)

(My psyche seems to be a little squirrely today)

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=========================================================================
Date:         Thu, 24 Feb 2000 12:37:16 +1300
Reply-To:     Exploring the Psyche in Cyberspace <PSYBER-L@HOME.EASE.LSOFT.COM>
Sender:       Exploring the Psyche in Cyberspace <PSYBER-L@HOME.EASE.LSOFT.COM>
From:         Walter Logeman <walter@PSYBERNET.CO.NZ>
Organization: psybernet
Subject:      Re: Weblog
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Sheryl

I'm glad you had a look...

> > http://psybernet.editthispage.com/

> I very briefly visited the site, but accessed it through the main Psybernet
> website because I couldn't get there from the URL.

Hmmm, I just got there from the URL above.  There is also a link
to it on the Psybernet website, and I like to think of it as part
of that.

> And I mean my visit was really brief... but it was intriguing enough to pull
> me out of a kind of psyber-l apathy to post today.  My impression of the site
> is almost hallucinogenic!  Entering into your stream of conscious thought,
> like *being Walter Logeman*?!

Wow!  I was exploring it to evaluate its possibility as a
repository for shared files, for a project separate from
Psybernet.  But I think it went beyond that as i see the
potential for livening up my website.  The technology allows me
to put up stuff on the web very easily -- one click! No FTP, no
HTML and so on.  Intriguing possibility!

So I have been flicking over whatever crossed my path... fairly
mundane stuff really.

> I have membership-to-the-site questions, though.  Will you sell my name to a
> spammer?  Will I start receiving invitations to enter other on-line psyche
> sites?  Will my email box become full of subject titles such as "Free on-line
> psyche 2MB reserved in your name?"  Will the word psyche somehow evolve into
> the word psychic and then I would get a whole raft of other worldly type
> spams too?

I would never sell addresses. I have posted out very occasional
stuff like the Invitation to the Dreamevent to people who were on
the last one.  I think of an email address as something very
personal to an individual, their representation in this world,
their avatar... selling them is really bad new imo.

I have a sense that the values of the developers of the software
for this Weblog are very principled.  However I will explore the
actual contract and settings and security... all this is
important as for my investigation in relation to using this for
Online Learning.  And very interesting for the implications of
the "temenos" for psychological work.

> If not, I think I might like to enter the discussion site.
>
> Sheryl  =:)

Come on over if you feel ok with it. We can learn together.

I think the discussion there could be interesting.  Not as
contained or focussed as here perhaps... (when it gets going that
is! :)

> (My psyche seems to be a little squirrely today)

You should be over here... autumn is coming a good time for
gathering nuts.

"gathering nuts"  interesting phrase I notice in relation to all
this!



Walter

__________________________________________________________________
Walter Logeman  walter@psybernet.co.nz  http://www.psybernet.co.nz

____________________________________________________________
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=========================================================================
Date:         Sat, 26 Feb 2000 12:55:54 +1300
Reply-To:     Exploring the Psyche in Cyberspace <PSYBER-L@HOME.EASE.LSOFT.COM>
Sender:       Exploring the Psyche in Cyberspace <PSYBER-L@HOME.EASE.LSOFT.COM>
From:         Walter Logeman <walter@PSYBERNET.CO.NZ>
Organization: psybernet
Subject:      An Interview with Stephen Talbott
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Psyber-l,


http://www.cgjung.com/psychtech/talbott1.html
Towards a Jungian Psychology of Technology

| An Interview with Stephen Talbott
|
| by Dolores Brien
|
| The thrust of Stephen Talbott's deeply thought and deeply felt
| work is to awaken us from our psychological somnambulism vis 
| vis the technology which permeates our personal life and culture.

Stephen Talbott intrigues me at this point, and i will follow up
on reading his articles and book etc.

He is stimulatingly anti technology, or is it simply tech wise,
as in street wise?

I often say "the Psyche can get you!" this is my sense of respect
for it, much like a climber needs to respect the mountains and
the weather.  Technology can get you!  No doubt!  And it can get
us as a culture, and does.

How is this for a sound byte of his:

"The computer is our hope if we can accept it as our enemy. As
our friend, it will destroy us."


Walter


There are more Talbott links on the Psybernet Weblog,
http://psybernet.editthispage.com/

PS  Thanks to those who have come to have a look! I have been
adding a bit of stuff... feel free to join in there, though...

For proper discussion i think a mailing list like this is the
best.  I would not want looses the focus (though we have been
very quiet here!).




__________________________________________________________________
Walter Logeman  walter@psybernet.co.nz  http://www.psybernet.co.nz

____________________________________________________________
Read, write in Psyber-L, manage your options, or unsubscribe
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=========================================================================
Date:         Sat, 26 Feb 2000 13:21:41 +1300
Reply-To:     Exploring the Psyche in Cyberspace <PSYBER-L@HOME.EASE.LSOFT.COM>
Sender:       Exploring the Psyche in Cyberspace <PSYBER-L@HOME.EASE.LSOFT.COM>
From:         Walter Logeman <walter@PSYBERNET.CO.NZ>
Organization: psybernet
Subject:      Networking on the Network
Comments: To: group-l@lists.groupsense.co.nz
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http://dlis.gseis.ucla.edu/people/pagre/network.html

| Networking on the Network
| From a doc by Phil Agre:
|
| "This is the version of 16 February 2000. Please send me any
| comments that might improve future versions, particularly if you
| have tried putting my advice into practice. I want to get
| "Networking on the Network" into the hands of every PhD student
| in the world. If you could help me out with this goal, I would
| much appreciate it."


My attention is usually on the psyche in some way.  However my
take on the psyche is that we are immersed in it.  No, it is not
on the surface of the world around us, but it the sum of our
projection of feeling, thoughts and images into that world.  We
might see God dancing in a plastic bag in the wind (great movie
American Beauty). Mostly we will see the psyche in other people.
Our "sociometric matrix" as Moreno called it. Soul is our
networks, seen soulfully.

That is why I am happy to put up links of a certain kind, they
somehow reflect *me* and the networks I see.  Phil Agre of RRE is
a bit of an inspiration to me with his ability to network.  Any
way, Brian if you read this, what do you think!

Walter

PS: also posted this
http://psybernet.editthispage.com/discuss/msgReader$47





__________________________________________________________________
Walter Logeman  walter@psybernet.co.nz  http://www.psybernet.co.nz

____________________________________________________________
Read, write in Psyber-L, manage your options, or unsubscribe
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