Evolutionary and incremental clustering techniques for analysis of dynamic networks: a comparative study

Abstract

Dynamic network analysis is a brewing topic of research these days and has gained significant importance because of its wide applicability in social media. A considerably large amount of dynamic data is available in social media and is significantly used to find out interesting relationships, patterns and trends. Clustering techniques have emerged as an efficient course to handle such dynamic network data. In this paper, we present a comparative study of various evolutionary and incremental clustering techniques specifically designed to handle the volatile nature of network data. We as authors have identified some key parameters based on which the clustering techniques are compared which will help in selection of the appropriate technique under particular conditions and scenarios.