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BiNeTClus: Bipartite Network Community Detection Based on Transactional Clustering

Published:13 November 2020Publication History
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Abstract

We investigate the problem of community detection in bipartite networks that are characterized by the presence of two types of nodes such that connections exist only between nodes of different types. While some approaches have been proposed to identify community structures in bipartite networks, there are a number of problems still to solve. In fact, the majority of the proposed approaches suffer from one or even more of the following limitations: (1) difficulty in detecting communities in the presence of many non-discriminating nodes with atypical connections that hide the community structures, (2) loss of relevant topological information due to the transformation of the bipartite network to standard plain graphs, and (3) manually specifying several input parameters, including the number of communities to be identified. To alleviate these problems, we propose BiNeTClus, a parameter-free community detection algorithm in bipartite networks that operates in two phases. The first phase focuses on identifying an initial grouping of nodes through a transactional data model capable of dealing with the situation that involves networks with many atypical connections, that is, sparsely connected nodes and nodes of one type that massively connect to all other nodes of the second type. The second phase aims to refine the clustering results of the first phase via an optimization strategy of the bipartite modularity to identify the final community structures. Our experiments on both synthetic and real networks illustrate the suitability of the proposed approach.

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    • Published in

      cover image ACM Transactions on Intelligent Systems and Technology
      ACM Transactions on Intelligent Systems and Technology  Volume 12, Issue 1
      Regular Papers
      February 2021
      280 pages
      ISSN:2157-6904
      EISSN:2157-6912
      DOI:10.1145/3436534
      Issue’s Table of Contents

      Copyright © 2020 ACM

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      Publication History

      • Published: 13 November 2020
      • Accepted: 1 September 2020
      • Revised: 1 July 2020
      • Received: 1 October 2019
      Published in tist Volume 12, Issue 1

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