Comparing algorithmic principles for fuzzy graph communities over Neo4j

Abstract

Fuzzy graphs occur frequently in diverse fields such as computational neuroscience, social network analysis, devops, and information retrieval. This chapter covers an important class of fuzzy graphs where vertices are fixed whereas edges are fuzzy and exist according to a given or estimated probability distribution. Empirical evidence strongly suggests that, similarly to their deterministic counterparts, large fuzzy graphs of this type consist of recursively nested communities. The latter are closely linked to efficient local information dissemination and processing. Two community discovery algorithms, namely Fuzzy Walktrap and Fuzzy Newman-Girvan, based on different algorithm design principles are proposed and the performance of their Java implementation over Neo4j is experimentally assessed in terms of both total execution time and average graph cut cost on synthetic and real fuzzy graphs.