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Algorithms 2016, 9(1), 8; doi:10.3390/a9010008

A Greedy Algorithm for Neighborhood Overlap-Based Community Detection

Computer Science, Jackson State University, Jackson, MS 39217, USA
Academic Editor: Tatsuya Akutsu
Received: 24 October 2015 / Revised: 1 January 2016 / Accepted: 6 January 2016 / Published: 11 January 2016
(This article belongs to the Special Issue Algorithms for Complex Network Analysis)
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The neighborhood overlap (NOVER) of an edge u-v is defined as the ratio of the number of nodes who are neighbors for both u and v to that of the number of nodes who are neighbors of at least u or v. In this paper, we hypothesize that an edge u-v with a lower NOVER score bridges two or more sets of vertices, with very few edges (other than u-v) connecting vertices from one set to another set. Accordingly, we propose a greedy algorithm of iteratively removing the edges of a network in the increasing order of their neighborhood overlap and calculating the modularity score of the resulting network component(s) after the removal of each edge. The network component(s) that have the largest cumulative modularity score are identified as the different communities of the network. We evaluate the performance of the proposed NOVER-based community detection algorithm on nine real-world network graphs and compare the performance against the multi-level aggregation-based Louvain algorithm, as well as the original and time-efficient versions of the edge betweenness-based Girvan-Newman (GN) community detection algorithm. View Full-Text
Keywords: community detection; edge betweenness; modularity score; neighborhood overlap; real-world network community detection; edge betweenness; modularity score; neighborhood overlap; real-world network

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Meghanathan, N. A Greedy Algorithm for Neighborhood Overlap-Based Community Detection. Algorithms 2016, 9, 8.

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