Social significance of community structure: Statistical view
Hui-Jia Li, J J.Daniels

TL;DR
This paper introduces a statistical framework for evaluating the significance of community structures in social networks, accounting for real-world complexities like randomness and errors, and applies it to both benchmark and real social data.
Contribution
It presents a novel method combining social hierarchy modeling, similarity measures, and p-value based significance testing for community analysis.
Findings
Effective in determining the optimal number of communities
Able to assess the social significance of communities
Useful for comparing different community detection algorithms
Abstract
Community structure analysis is a powerful tool for social networks, which can simplify their topological and functional analysis considerably. However, since community detection methods have random factors and real social networks obtained from complex systems always contain error edges, evaluating the significance of community structure partitioned is an urgent and important question. In this paper, integrating the specific characteristics of real society, we present a novel framework analyzing the significance of social community specially. The dynamics of social interactions are modeled by identifying social leaders and corresponding hierarchical structures. Instead of a direct comparison with the average outcome of a random model, we compute the similarity of a given node with the leader by the number of common neighbors. To determine the membership vector, an efficient community…
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