Non-Parametric Detection of Network Communities; The Natural Way; A Cascaded Stackelberg Game

TL;DR

This paper introduces CASCODE, a non-parametric community detection algorithm inspired by Stackelberg games, capable of naturally identifying evolving communities and their number in dynamic networks without prior assumptions.

Contribution

The paper presents a novel Stackelberg game-based algorithm for community detection that learns the number of communities automatically and improves resolution in dense networks.

Findings

Effectively detects evolving communities in dynamic networks.

Learns the number of communities without prior input.

Achieves finer community resolution in dense networks.

Abstract

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.