A survey of community detection algorithms based on analysis-intent

Abstract

There has been a significant amount of research dedicated to identifying community structures within graphs. Most of these studies have focused on partitioning techniques and the resultant quality of discovered groupings (communities) without regard for the intent of the analysis being conducted (analysis-intent). In many cases, a given network community can be composed of significantly different elements depending upon the context in which a partitioning technique is used or applied. Moreover, the number of communities within a network will vary greatly depending on the analysis-intent and thus the discretion quality and performance of algorithms will similarly vary. In this survey we review several algorithms from the literature developed to discover community structure within networks. We review these approaches from two analysis perspectives: role/process focused (category-based methods) and topological structure or connection focused (eventbased methods). We discuss the strengths and weaknesses of each algorithm and provide suggestions on the algorithms’ use depending on analysis context.