A generalised significance test for individual communities in networks

Sadamori Kojaku, Naoki Masuda*

*この研究の対応する著者

研究成果: Article査読

11 被引用数 (Scopus)

抄録

Many empirical networks have community structure, in which nodes are densely interconnected within each community (i.e., a group of nodes) and sparsely across different communities. Like other local and meso-scale structure of networks, communities are generally heterogeneous in various aspects such as the size, density of edges, connectivity to other communities and significance. In the present study, we propose a method to statistically test the significance of individual communities in a given network. Compared to the previous methods, the present algorithm is unique in that it accepts different community-detection algorithms and the corresponding quality function for single communities. The present method requires that a quality of each community can be quantified and that community detection is performed as optimisation of such a quality function summed over the communities. Various community detection algorithms including modularity maximisation and graph partitioning meet this criterion. Our method estimates a distribution of the quality function for randomised networks to calculate a likelihood of each community in the given network. We illustrate our algorithm by synthetic and empirical networks.

本文言語English
論文番号7351
ジャーナルScientific reports
8
1
DOI
出版ステータスPublished - 2018 12 1
外部発表はい

ASJC Scopus subject areas

  • 一般

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