Abstract：
:Finding overlapping communities of complex networks remains a challenge in network science. To address this challenge, one of the widely used approaches is finding the communities of links by optimizing the objective function, partition density. In this study, we show that partition density suffers from inverse resolution limit; it has a strong preference to triangles. This resolution limit makes partition density an improper objective function for global optimization. The conditions where partition density prefers triangles to larger link community structures are analytically derived and confirmed with global optimization calculations using synthetic and real-world networks. To overcome this limitation of partition density, we suggest an alternative measure, Link Surprise, to find link communities, which is suitable for global optimization. Benchmark studies demonstrate that global optimization of Link Surprise yields meaningful and more accurate link community structures than partition density optimization.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Lee J,Zhang ZY,Lee J,Brooks BR,Ahn YYdoi
10.1038/s41598-017-12432-1subject
Has Abstractpub_date
2017-09-29 00:00:00pages
12399issue
1issn
2045-2322pii
10.1038/s41598-017-12432-1journal_volume
7pub_type
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