Web-based Learning Solutions for Communities of Practice

approaches concerning social networking analysis,
some of them will be mentioned. Closeness
(Sabidussi, 1966) and betweenness (Freeman,
1977), (Anthonisse, 1971) are two measures of
the centrality of vertex within a graph and can be
computed with well established algorithms that
are tightly coupled with common shortest path
algorithms (e.g. graph traversal using Djikstra
algorithm). From an implementation point of view,
LEDA (Mehlhorn & Näher, 1999) is a general
scope library which provides adequate support
to extract the required computations.
As a second step of analysis, and after the
clustering procedure, we have groups of users that
have the same or closely related properties in different
levels for each CoP. In particular, consider
a partition of the CoP into C1, C2,..., Ck and that
for a community Ci there is a division of members
into the following set of clusters, Cluster i2 n, ...,
Cluster im (let n i1 , n i2 , ..., n im be the number of
members in each cluster). Then, we can compute
the values of each Social Network Analysis measures
for every single community of practice by
properly adjusting the various formulas taking
into account the values of the parameters in each
cluster. This type of analysis provides different
results than the results of the previous step. After
clustering, we can examine each cluster separately
and then consider a sub-graph of the initial graph
where we take into account only users that belong
to this cluster. It is obvious that now the measures
of the metrics are different, and we can extract
different types of observations. For example a
specific group of users within the same community
considered to have high connectivity and
thus may appear as a cluster is actually a “weak”
cluster, where in reality only a part of this cluster
is the “knot sum”. For instance, we can find out
users of the same cluster that already share some
common characteristics, and were not communicating
as expected. The system can make use of
the outcome of our analysis and provide links or
notifications to the users of the same cluster by
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Mining Unnoticed Knowledge in Collaboration Support Systems
emphasizing the users with low communication
flow and highlighting their activity.
Having these groups of users (clusters) we can
also perform cluster analysis. This analysis can
give a better understanding about the relations
between users especially in this type of systems
where we have multiple types of data. Also it can
help detect outliers (objects from the data that have
different distribution of values from the majority
of other objects in the space), which may emerge
as singletons or as small clusters far from the
others. For a given intensity of communication or
actions, clusters emphasize the various intensity
levels. Cluster analysis also reveals useful information
for interpretation. We can find how many
groups are in the same community and each group
can be inspected to reveal patterns of interest.
In this phase, we can make specific analysis in
each cluster and measure the metrics mentioned
before. In this way, we can have different type
of analysis. For instance, consider some users
that belong to the same community and have a
relationship, but their actions in this community
are limited. In this case, clustering can assist by
grouping them into the same cluster and evaluate
the metrics just for them. Otherwise, we cannot
have the opportunity to analyze just the users that
share some common characteristics (like the users
of this example) and their relationship would not
be so easy to be revealed.
SUMMARY
A CoP may be regarded as a dynamic organization.
Like any other network, there are some parts which
may have crucial impact on its efficiency. The
identification of a set of qualitative metrics that
describe the above network can be useful to help
members work more adequately and guarantee the
CoP continuity. The framework we describe may
be applied to a large number of systems designed to
support collaboration and may help people identify
information that was not obvious from every day