Visualization of Communication Patterns in Collaborative
Innovation Networks
Analysis of some W3C working groups
Peter A. Gloor
1,2
, Rob Laubacher
1
, Scott B.C. Dynes
2
, Yan Zhao
3
1
MIT Center for Coordination Science,
2
Dartmouth Tuck Center for Digital Strategies,
3
Dartmouth College
Dept. of Computer Science
Abstract
Collaborative Innovation Networks (COINs) are
groups of self-motivated individuals from
various parts of an organization or from multiple
organizations, empowered by the Internet, who
work together on a new idea, driven by a
common vision. In this paper we report first
results of a project that examines innovation
networks by analyzing the e-mail archives of
some W3C (WWW consortium) working groups.
These groups exhibit ideal characteristics for our
purpose, as they form truly global networks
working together over the Internet to develop
next generation technologies. We first describe
the software tools we developed to visualize the
temporal communication flow, which represent
communication patterns as directed acyclic
graphs. We then show initial results, which
revealed significant variations between the
communication patterns and network structures
of the different groups. We were also able to
identify distinctive communication patterns
among group leaders, both those who were
officially appointed and other who were
assuming unofficial coordinating roles.
Keywords: collaborative innovation network,
social network analysis, information
visualization, knowledge management,
collaborative applications.
1. Introduction
Collaborative Innovation Networks (COINs) are
groups of self-motivated individuals from
various parts of an organization or from multiple
organizations, empowered by the Internet, who
work together on a new idea, driven by a
common vision. The COIN diagnostic project is
a research effort co-located at the MIT Sloan
Center for Coordination Science and the
Dartmouth Tuck Center for Digital Strategies in
collaboration with other research centers. The
mission of the COIN diagnostic project is to
understand the ways people join COINs, how
COINs function and what COINs contribute to
enterprises by:
1. Analyzing electronic interaction logs such as
email to find COINs within organizations;
2. Identifying structural properties and
parameters of successful COINs;
3. Finding the people that make a COIN
successful by identifying the role profiles
crucial for the success of COINs;
4. Defining a metric to measure the success of
COINs;
5. Developing a framework and set of
organizational guidelines that can help
nurture and foster COINs within and across
organizations and make them more
effective.
2. Algorithm and System
Architecture
Just as Google is very effective at finding
pertinent documents based on linking patterns,
we believe analysis of e-mail and other
interaction logs of organizations will enable one
to discern the structure of networks and identify
core contributors. We propose a new
methodology: mining computer logs such as
05/28/03 - 2
email archives to trace the emergence of COINs
and their development over time. Our system
computes and visualizes the structure of existing
COINs by automatically generating a directed
graph of communication flows.
Figure 1. COIN e-mail analysis system
architecture
We are implementing a flexible three-level
architecture (figure 1): In the first step, the e-
mail messages are parsed and stored in
decomposed format in a SQL database. In the
second step the database is queried to select
messages sent and/or received by a group in a
time period. In the third step the selected
communication flows are visualized using SNA
visualization tools such as Pajek (Batagelj &
Mrvar, 1998) and ucinet (Borgatti et al., 1992) or
our own communication flow visualization
applets.
This architecture provides a testbed with high
scalability and flexibility: the number of
messages to be analyzed is only limited by the
size of the database, and temporal queries can be
run in an ad hoc way. We are also able to
experiment with different visualizations of the
retrieved structure.
Figure 2 contains our community visualization
applet in the generic mode, where all
relationships are visualized. Each node on the
screen represents a person, and e-mails
exchanged are visualized as arcs. The proximity
of nodes is driven by the number of messages
they have exchanged. The most active senders
and receivers are depicted in the center of the
graph.
Figure 2. COIN visualization applet
The visualizations in figures 2 employ the
Fruchterman-Reingold graph drawing algorithm
(Fruchterman & Reingold, 1991) commonly
used to visualize social networks. This method
compares a graph to a mechanical collection of
electrically charged rings (the vertices) and
connecting springs (the edges). Every two
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vertices reject each other by a repulsive force
and adjacent vertices (connected by an edge) are
pulled together by an attractive force. Over a
number of iterations the forces modeled by the
springs are calculated and the nodes are moved
to minimize the forces felt. In our visualizations,
the attraction between connected nodes is scaled
to the number of messages exchanged.
Figure 3. COIN visualization applet in
personalized mode
Figure 3 illustrates the COIN visualization applet
in personalized mode. Clicking on a node
displays all the links going to or originating from
a particular person. “To”, “Cc”, and “From”
links can be filtered separately.
Figure 4. COIN visualization applet in subject
mode
Figure 4 illustrates the COIN visualization applet
in subject mode, where the message flow on a
certain subject (as indicated in the “Subject” line
of the message) can be visualized. Messages can
be further sub-selected by person and by month.
Figure 5. COIN animation applet
Figure 5 illustrates the COIN animation applet
view. This applet displays a movie of the
communication flow over time, where all active
links in an adjustable time-window, ranging
from 5 to 100 days, are shown. The user can
single-step through the time-windows using a
slider, or play back the communication activity
of the whole year as a movie.
3. Selection of the Communities
We compared three similar communities,
exhibiting strong COIN characteristics. They
share the goal of furthering the development of
the Web under the auspices of the WWW
consortium (W3C, www.w3.org). W3C activities
are generally organized into working groups.
These groups, made up of representatives from
W3C member organizations, the W3C core team,
and invited experts, produce the bulk of W3C's
results: technical reports, open source software,
and services such as validation services. These
groups also ensure coordination with other
standards bodies and technical communities.
There are currently over thirty W3C working
groups. W3C is keeping a public archive of the
e-mail communication of the ongoing
discussions of the working groups at
http://lists.w3.org/Archives/Public/. In our work
05/28/03 - 4
we analyzed the mailing list archives of three
W3C working groups:
Group A
Group A is made up of Web enthusiasts, who
contribute to the group because they are
interested into the further development of the
Web. There is little encouragement by large
companies for their employees to participate in
this group, so it is made up of academics and
company researchers working on their own time
and representing their own opinion. The
motivation to participate is the recognition of
their work by peers. We were able to analyze
data from Group A for 1999 to 2003.
Group B
The goal of Group B is to develop a Web
standard with high commercial value to large
software companies. There are two sets of people
active in this group. The first consists of
software company representatives, who are
usually appointed by their firm and have to
represent the viewpoint of their employer. This
means that firm management has a close eye on
the ongoing communication of this group, as
there are diverging interests of the companies
sending their representatives. The second set of
people in this group are consultants and
academics, who participate out of personal
interest or to create consulting opportunities by
making a name for themselves. The standard
covered by this group is fairly new, so we could
only obtain data for Group B from 2002 to 2003.
Group C
Group C is also composed of two sets of people.
One set consists of a core group selected by the
W3C and its member firms to to discuss and
further develop the technical architecture of the
Web. The second set are academics and
consultants who participate either because they
are genuinely interested or because they want to
build a reputation. The goal of the group is of
somewhat lesser commercial impact than that of
Group B. This group has been active from 2002
to 2003.
4. Analysis of Networks
One initial finding is that each group had
between 150 and 200 members when fully
active. This finding corresponds to results from
past work by ethnologists that 150-200 is the
largest sized group that can work together
productively before fragmenting (Gladwell
2002).
We used several metrics developed by social
network analysts to compare the three W3C
working groups: density, betweenness centrality,
and group degree centrality.
Density (Wassermann & Faust, 1994) is the
proportion of potential arcs in the graph that are
actually connected, a measure which can range
between 0 and 1.
Centrality of an actor is a measure for its
importance. The simplest measure of centrality is
the number of arcs one node has to other nodes
(known as the node’s degree).
Group degree centrality measures the similarity
in the communication pattern among different
group members. It is 1 for a group where one
actor communicates with all others in a star
configuration, and 0 for a circle where
everybody communicates with everybody.
Betweenness of actors is a measure for the
interpersonal influence they have on others, by
being “between” other actors. Freeman (1979)
defines group betweenness centrality as a
measure for the homogeneity of betweenness of
different actors. Betweenness centrality is 1 in a
star configuration, and 0 if all actors have the
same degree of betweenness. Lower betweeness
centrality means the communication behavior of
the group members is more egalitarian.
For our three groups, we obtained the following
results for 2002:
2002
Density
Group
Degree
Centrality
Group
Between-
ness
Centrality
Group A
0.06
0.29
0.13
Group B
0.12
0.41
0.20
Group C
0.10
0.46
0.18
We notice that Group A exhibits significantly
lower betweenness centrality and group degree
centrality than the other two groups.
05/28/03 - 5
We can speculate that these differences may be
due to the nature of the tasks being carried out by
the different groups. The more focused tasks of
Group B and C, for example development and
gaining agreement on standards, could mean
more hierarchical group interactions. Another
possible reason for different group
communication patterns might be the
characteristics of the groups’ members. The
corporate researchers in Groups B and C may be
more accustomed to hierarchical interactions
than the university researchers in Group A, who
are more used to interacting with other university
researchers as peers.
Our animations suggested further differences
between the groups’ communication patterns.
For example, the animation for one group
showed the emergence of a core that
communicated with high frequency among
themselves, while outlying members contributed
only sporadically. It appeared that a large group
of peripheral members were only weakly
connected to the core group. Another group
exhibited more egalitarian behavior, where
“everybody was talking to everybody.” We plan
to undertake future work which will allow us to
analyze such differences in greater detail.
5. Analysis of Individual
Communication Behavior
From the communication patterns, it was
possible to identify a group of leaders in the
COINs we analyzed. We were also able to
identify contributors who had assumed
leadership roles without having been officially
appointed.
For Group C we looked at the message pattern of
the 9 appointed leaders. We only analyzed
threads that contain more than five messages.
This allowed us to filter out requests for
information and other insignificant exchanges.
As was expected, the leaders showed up in the
center of the graph (indicated as large nodes),
together with other significant contributors
(figure 6).
We also noticed a difference within the
leadership group along two dimensions:
contribution frequency (measured in the numbers
of messages sent), and the extent to which their
communication was balanced between sending
and receiving messages, which we measured via
a simple contribution index:
messages sent – messages received
total of messages sent and received
This index is –1 for somebody who only receives
messages, 0 for somebody who sends and
receives the same number of messages, and +1
for somebody who only sends messages.
Figure 6. Group C in 2003 with 9 leaders
(leaders indicated with large squares)
Figure 7 illustrates communication patterns for
leaders in Group C. The dark rectangles in the
center are the W3C leaders. The large company
representatives are the lightly shaded rectangles.
The leaders appointed by the W3C were among
the most active contributors and also exhibited
balanced communication behavior, with a
contribution index close to 0. The representatives
of the large IT companies participated much less
in absolute numbers, and some of them sent
substantially more messages than they received.
There were also some participants who were
even more active than the officially appointed
leaders. These members appeared to be
assuming a self-appointed leadership role.
Preliminary analysis indicated that such
unofficial leaders also emerged in other groups.
We intend to undertake further analysis to
identify how these unofficial leaders come to
assume this role.
