On converting community detection algorithms for fuzzy graphs in Neo4j

An essential feature of large scale free graphs, such as the Web, protein-to-protein interaction, brain connectivity, and social media graphs, is that they tend to form recursive communities. The latter are densely connected vertex clusters exhibiting quick local information dissemination and processing. Under the fuzzy graph model vertices are fixed while each edge exists with a given probability according to a membership function. This paper presents Fuzzy Walktrap and Fuzzy Newman-Girvan, fuzzy versions of two established community discovery algorithms. The proposed algorithms have been applied to a synthetic graph generated by the Kronecker model with different termination criteria and the results are discussed. Keywords-Fuzzy graphs; Membership function; Community detection; Termination criteria; Walktrap algorithm; NewmanGirvan algorithm; Edge density; Kronecker model; Large graph analytics; Higher order data