Enriching networks with edge insertion to improve community detection
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 th...
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Published in | Social network analysis and mining Vol. 11; no. 1; p. 89 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Vienna
Springer Vienna
01.12.2021
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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ISSN: | 1869-5450 1869-5469 |
DOI: | 10.1007/s13278-021-00803-6 |