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Inverse Resolution Limit of Partition Density and Detecting Overlapping Communities by Link-Surprise

Cited 6 time in Web of Science Cited 8 time in Scopus
Authors

Lee, Ju Yong; Zhang, Zhong Yuan; Lee, Joo Young; Brooks, Bernard R.; Ahn, Yong-Yeol

Issue Date
2017-09
Publisher
Nature Publishing Group
Citation
Scientific Reports, Vol.7, p. 12399
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.
ISSN
2045-2322
URI
https://hdl.handle.net/10371/201522
DOI
https://doi.org/10.1038/s41598-017-12432-1
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  • Graduate School of Convergence Science & Technology
  • Dept. of Molecular and Biopharmaceutical Sciences
Research Area AI models for drug discovery, Free energy calculation, Molecular dynamics, 분자동역학, 신약개발을 위한 AI 모델, 자유에너지 계산

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