Community Analysis in Dynamic Social Networks

Due to the growing popularity of social networking platforms, the analysis of
online communities became in recent years a popular topic in different research
fields. However, an aspect that has received only little attention is how the
temporal aspects of social networks can be studied. This thesis bridges the gap and
deals with the analysis of community structures in large social networks and their
temporal dynamics. Two clustering techniques are proposed to detect communities
in social networks and to study the evolution of these structures over time. The
two approaches basically differ in the underlying definition of what constitutes a
community over time: In the first case, a community is considered a subgroup
that can be observed over time and a hierarchical edge betweenness clustering
approach is proposed to detect such communities. In the second case, a community
is defined as a subgroup that evolves over time and an incremental density-based
clustering algorithm is proposed to detect and track these evolving communities.
The applicability of the proposed approaches is evaluated by applying them to
different real world data sets. The obtained results indicate that the introduced
clustering techniques are appropriate to efficiently detect online communities in
large social networks and to track their evolution over time.