Community detection in hypernetwork via Density-Ordered Tree partitionAuthor-Name: Cheng, Qing

Zhong Liu, Jincai Huang and Guangquan Cheng

Applied Mathematics and Computation, 2016, vol. 276, issue C, 384-393

Abstract: Hypernetwork, as a useful representation of natural and social systems has received increasing interests from researchers. Community is crucial to understand the structural and functional properties of the hypernetworks. Here, we propose a new method to uncover the communities of hypernetworks. We construct a Density-Ordered Tree (DOT) to represent original data by combining density and distance, and the community detection in hypernetwork is converted to a DOT partition problem. Then, an anomaly detection strategy using box-plot rule is applied to partition DOT and judge whether there is a significant community structure in the hypernetwork. Moreover, visual inspection as a complementary approach of box-plot rule can effectively improve the effectiveness of community detection. Finally, the method is compared with existing methods in both synthetic and real-world networks.

Keywords: Community; Density-Ordered Tree; Anomaly detection; Visual inspection (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:276:y:2016:i:c:p:384-393

DOI: 10.1016/j.amc.2015.12.039

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