High-Quality Disjoint and Overlapping Community Structure in Large-Scale Complex Networks

Abstract

In this paper, we propose an improved version of an agglomerative hierarchical clustering algorithm that performs disjoint community detection in large-scale complex networks. The improved algorithm is achieved after replacing the local structural similarity used in the original algorithm, with the recently proposed Dynamic Structural Similarity. Additionally, the improved algorithm is extended to detect fuzzy and crisp overlapping community structure. The extended algorithm leverages the disjoint community structure generated by itself and the dynamic structural similarity measures, to compute a proposed membership probability function that defines the fuzzy communities. Moreover, an experimental evaluation is performed on reference benchmark graphs in order to compare the proposed algorithms with the state-of-the-art.

arXiv comment8 pages, 5 figures, 3 tables, sent to peer-review to the International Symposium on Foundations and Applications of Big Data Analytics FAB 2018

References in corpus (12)

• Finding and evaluating community structure in networks
• Maps of random walks on complex networks reveal community structure
• Near linear time algorithm to detect community structures in large-scale networks
• Benchmark graphs for testing community detection algorithms
• Resolution limit in community detection
• Detecting highly overlapping community structure by greedy clique expansion
• Normalized Mutual Information to evaluate overlapping community finding algorithms
• SLPA: Uncovering Overlapping Communities in Social Networks via A Speaker-listener Interaction Dynamic Process
• Leveraging disjoint communities for detecting overlapping community structure
• Community Detection via Local Dynamic Interaction
• Improving PageRank for Local Community Detection
• Dynamic Structural Similarity on Graphs