Community detection in complex networks based on DBSCAN* and a Martingale process

Abstract

Community detection is a valuable tool for analyzing and understanding the structure of complex networks. This work investigates the application of the density-based algorithm DBSCAN* to the community detection problem. Given, though, that this algorithm requires a lower bound for the community size to be determined a priori, this work proposes the application of a Martingale process to DBSCAN* so as to progressively detect communities at various levels of granularity, without the need to define in advance a single threshold for the minimum community size. In particular, the proposed DBSCAN*-Martingale community detection algorithm corresponds to an iterative process that progressively lowers the threshold of the size of the acceptable communities, while maintaining the communities detected for higher thresholds. Evaluation experiments are performed based on four realistic benchmark networks and the results indicate improvements in the effectiveness of the proposed DBSCAN*-Martingale community detection algorithm in terms of the Normalized Mutual Information and RAND metrics against several state-of-the-art community detection approaches.