Title

Overlapping Community Detection via Minimum Spanning Tree Computations

Publication Date

8-1-2020

Document Type

Conference Proceeding

Department

Computer Science

Publication Title

2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService)

DOI

10.1109/BigDataService49289.2020.00017

First Page

62

Last Page

65

Abstract

Contemporary social networks deal with Big Data in which a large amount of useful information is hidden. Detecting communities in such networks constitutes a particularly challenging computational task. In this paper, we propose an algorithm for detecting overlapping communities, which builds on an hierarchical divisive method called ST (AlgoCloud2018), originally designed to detect disjoint communities efficiently and without significant loss of information. The method is based on first computing a minimum spanning tree of the original graph and then calculating the edge and vertex betweenness centrality measures on the tree, considerably speeding up calculations.

Keywords

Big Data, Community Detection, Edge Betweenness, Modularity, Neighborhood Overlapping, Overlapping Communities, Social Network, Spanning Trees, Split Betweenness

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