Local Higher-Order Community Detection Based on Fuzzy Membership Functions

Tao Meng,Lijun Cai,Ziyun Deng,Tingqin He,Lei Chen

doi:10.1109/access.2019.2939535

Abstract

Local community detection, only considering the regional information of the large network, can be used to identify a densely connected community containing the seed node in a network, aiming to address the efficiency problem faced by global community detection. However, most existing studies in local community detection did not account for the higher-order structures crucial to the network, but rather have simply focused single nodes or edges. Moreover, existing higher-order solutions are not purely local methods, as they still use global search to find the best local community, which leads to a global search problem. Furthermore, the quality of the detected community depends on the location of the seed node, which leads to a seed-dependent problem. Thus, in this paper, we proposed a fuzzy agglomerative algorithm (FuzLhocd) for local higher-order community detection based on different fuzzy membership functions. To solve the global search problem, we introduce a novel, purely localized metric called local motif modularity. Based on this local metric, FuzLhocd only needs to visit a limited number of neighborhoods around the seed node. To solve the seed-dependent problem, we systematically studied the formation of the local community, divided the process of local community detection into three stages and employed various fuzzy membership functions at different stages. Our extensive experiments based on both real-world and synthetic networks demonstrated that FuzLhocd not only runs efficiently locally but also effectively solves the seed-dependent problem and achieves a high accuracy as well. We concluded that our local motif modularity metric and FuzLhocd algorithm is highly effective for local higher-order community detection.

Highlights

Community structures naturally exist in many real-world networks such as social networks, collaboration networks, biological networks and other types of complex networks [1]–[4]
Local community detection is a fundamental problem in complex network analysis and has attracted intensive research interest
Most existing local community detection methods are based on a single node or edge, thereby ignoring the higher-order structures that are important for networks of a given domain

Summary

Introduction

Community structures naturally exist in many real-world networks such as social networks, collaboration networks, biological networks and other types of complex networks [1]–[4]. The aim of community detection is to identify all communities in a global network, which has been widely studied in the literature and remains a fundamental problem in complex network analysis [5]–[7]. It is often expensive (even no way) to obtain the global information of the network in many real-world networks of increasing size [8], [9]. The goal of the friend recommendation feature of WeChat is to recommend candidate friends to a specific user, u Only those who are in the same community as u are but who are not yet u’s friends are suggested [11].

Methods

Results

Conclusion