Enriching networks with edge insertion to improve community detection

• Published in: Social network analysis and mining Vol. 11; no. 1; p. 89
• Main Authors: de Oliveira, Éric Tadeu Camacho, de França, Fabrício Olivetti
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01.12.2021; Springer Nature B.V
• Subjects: Algorithms; Applications of Graph Theory and Complex Networks; Community; Computer Science; Data Mining and Knowledge Discovery; Economics; Eigenvectors; Game Theory; Heuristic; Humanities; Insertion; Law; Methodology of the Social Sciences; Modularity; Network analysis; Original Article; Social and Behav. Sciences; Social networks; Statistics for Social Sciences; Link prediction; Community detection; Enriched networks
• DOI: 10.1007/s13278-021-00803-6
• ISSN: 1869-5450

Community detection is a broad area of study in network science, in which its correct detection helps to get information about the groups and the relationships between their nodes. Community detection algorithms use the available snapshot of a network to detect its underlying communities. But, if this snapshot is incomplete, the algorithms may not recover the correct communities. This work proposes a set of link prediction heuristics using different network properties to estimate a more complete version of the network and improve the community detection algorithms. Each heuristic returns the most likely edges to be observed in a future version of the network. We performed experiments on real-world and artificial networks with different insertion sizes, comparing the results with two approaches: (i) without using edge insertion and (ii) using the EdgeBoost algorithm, based on node similarity measures. The experiments show that some of our proposed heuristics improve the results of traditional community detection algorithms. This improvement is even more prominent for networks with poorly defined structures.