  In this paper a simple but efficient real-time detecting algorithm is
proposed for tracking community structure of dynamic networks. Community
structure is intuitively characterized as divisions of network nodes into
subgroups, within which nodes are densely connected while between which they
are sparsely connected. To evaluate the quality of community structure of a
network, a metric called modularity is proposed and many algorithms are
developed on optimizing it. However, most of the modularity based algorithms
deal with static networks and cannot be performed frequently, due to their high
computing complexity. In order to track the community structure of dynamic
networks in a fine-grained way, we propose a modularity based algorithm that is
incremental and has very low computing complexity. In our algorithm we adopt a
two-step approach. Firstly we apply the algorithm of Blondel et al for
detecting static communities to obtain an initial community structure. Then,
apply our incremental updating strategies to track the dynamic communities. The
performance of our algorithm is measured in terms of the modularity. We test
the algorithm on tracking community structure of Enron Email and three other
real world datasets. The experimental results show that our algorithm can keep
track of community structure in time and outperform the well known CNM
algorithm in terms of modularity.
