On converting community detection algorithms for fuzzy graphs in Neo4j
Abstract
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.
Cite
@article{arxiv.1608.02235,
title = {On converting community detection algorithms for fuzzy graphs in Neo4j},
author = {Georgios Drakopoulos and Andreas Kanavos and Christos Makris and Vasileios Megalooikonomou},
journal= {arXiv preprint arXiv:1608.02235},
year = {2017}
}
Comments
Certain errors in the algorithms must be corrected