Overlapping Community Detection via Minimum Spanning Tree Computations
Publication Date
8-1-2020
Document Type
Conference Proceeding
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
Department
Computer Science
Recommended Citation
Aris Pagourtzis, Dora Souliou, Petros Potikas, and Katerina Potika. "Overlapping Community Detection via Minimum Spanning Tree Computations" 2020 IEEE Sixth International Conference on Big Data Computing Service and Applications (BigDataService) (2020): 62-65. https://doi.org/10.1109/BigDataService49289.2020.00017
