  Real-World networks have an inherently dynamic structure and are often
composed of communities that are constantly changing in membership. Identifying
these communities is of great importance when analyzing structural properties
of networks. Hence, recent years have witnessed intense research in of solving
the challenging problem of detecting such evolving communities. The mainstream
approach towards community detection involves optimization of a global
partition quality metric (e.g. modularity) over the network. Another technique,
Spectral Clustering, involves mapping of original data points in a lower
dimensional space, where the clustering properties of a graph are much more
evident, and then applying standard clustering techniques for identifying
communities. However, the traditional spectral clustering techniques cannot
naturally learn the number of communities in networks. These techniques are
based on external community connectivity properties, and often fail to identify
smaller community structures in dense networks. In this article, we propose an
algorithm, namely, the Cascaded Stackelberg Community Detection Algorithm
(CASCODE) inspired by the Stackelberg Duopoly Game. This algorithm uses the
notion of a leader-follower relationship between the nodes to influence the
actions of either. The intuition of the algorithm is based on the natural
expected internal structure in evolving communities in networks. Thus, the
algorithm is able to naturally learn the number of communities in a network in
contrast with other techniques such as Spectral Clustering, which require the
expected number of communities as an input. Because this Stackelberg
Model-based Community Detection algorithm detects communities through their
internal structure, we are able to obtain a finer community structure
resolution in dense networks.
