41 research outputs found
Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership
Detecting community structure in social networks is a fundamental problem
empowering us to identify groups of actors with similar interests. There have
been extensive works focusing on finding communities in static networks,
however, in reality, due to dynamic nature of social networks, they are
evolving continuously. Ignoring the dynamic aspect of social networks, neither
allows us to capture evolutionary behavior of the network nor to predict the
future status of individuals. Aside from being dynamic, another significant
characteristic of real-world social networks is the presence of leaders, i.e.
nodes with high degree centrality having a high attraction to absorb other
members and hence to form a local community. In this paper, we devised an
efficient method to incrementally detect communities in highly dynamic social
networks using the intuitive idea of importance and persistence of community
leaders over time. Our proposed method is able to find new communities based on
the previous structure of the network without recomputing them from scratch.
This unique feature, enables us to efficiently detect and track communities
over time rapidly. Experimental results on the synthetic and real-world social
networks demonstrate that our method is both effective and efficient in
discovering communities in dynamic social networks
Importance of individual events in temporal networks
Records of time-stamped social interactions between pairs of individuals
(e.g., face-to-face conversations, e-mail exchanges, and phone calls)
constitute a so-called temporal network. A remarkable difference between
temporal networks and conventional static networks is that time-stamped events
rather than links are the unit elements generating the collective behavior of
nodes. We propose an importance measure for single interaction events. By
generalizing the concept of the advance of event proposed by [Kossinets G,
Kleinberg J, and Watts D J (2008) Proceeding of the 14th ACM SIGKDD
International conference on knowledge discovery and data mining, p 435], we
propose that an event is central when it carries new information about others
to the two nodes involved in the event. We find that the proposed measure
properly quantifies the importance of events in connecting nodes along
time-ordered paths. Because of strong heterogeneity in the importance of events
present in real data, a small fraction of highly important events is necessary
and sufficient to sustain the connectivity of temporal networks. Nevertheless,
in contrast to the behavior of scale-free networks against link removal, this
property mainly results from bursty activity patterns and not heterogeneous
degree distributions.Comment: 36 pages, 13 figures, 2 table