A Node Similarity and Community Link Strength-Based Community Discovery Algorithm

Haijuan Yang,Ke Yang,Wenbo Zhang,Xiaoyun Chen,Zeyi Yang,Handong Zhang,Jianjun Cheng,Hocine Cherifi

doi:10.1155/2021/8848566

Abstract

Community structure is one of the common characteristics of complex networks. In the practical work, we have noted that every node and its most similar node tend to be assigned to the same community and that two communities are often merged together if there exist relatively more edges between them. Inspired by these observations, we present a community-detection method named NSCLS in this paper. Firstly, we calculate the similarities between any node and its first- and second-order neighbors in a novel way and then extract the initial communities from the network by allocating every node and its most similar node to the same community. In this procedure, some nodes located at the community boundaries might be classified in the incorrect communities. To make a redemption, we adjust their community affiliations by reclassifying each of them into the community in which most of its neighbors have been. After that, there might exist relatively larger number of edges between some communities. Therefore, we consider to merge such communities to improve the quality of the final community structure further. To this end, we calculate the link strength between communities and merge some densely connected communities based on this index. We evaluate NSCLS on both some synthetic networks and some real-world networks and show that it can detect high-quality community structures from various networks, and its results are much better than the counterparts of comparison algorithms.

Highlights

Complex networks are widely used to represent systems in the real world, and they often exhibit a structural characteristic of community structure, where nodes can be divided into groups naturally with much denser connections within groups than between groups. e community structure is a meaningful property of networks which can reflect the interactions of system components at the mesoscale level
We devise a novel method to address the problem in this paper, which is inspired by the phenomena observed in our practical work that each node, and its most similar node tend to be assigned to the same community, and two communities with many edges between them tend to be merged together to form a larger community
For some nodes located at the community boundaries, their current community affiliations might be irrational

Summary

Introduction

Complex networks are widely used to represent systems in the real world, and they often exhibit a structural characteristic of community structure, where nodes can be divided into groups naturally with much denser connections within groups than between groups. e community structure is a meaningful property of networks which can reflect the interactions of system components at the mesoscale level. Complex networks are widely used to represent systems in the real world, and they often exhibit a structural characteristic of community structure, where nodes can be divided into groups naturally with much denser connections within groups than between groups. Communities can be groups of scientific papers in citation networks [1], sharing same topics, and this characteristic can help to discover some newborn or interdisciplinary studies. Erefore, detecting communities can shed light on exploring and utilizing the relationships between the structural characteristics and the functional modules. Community detection becomes a hot spot in complex network studies in recent years. We concern the problem of detecting nonoverlapping communities here. We call the proposed method NSCLS (acronym for Node Similarity and Community Link Strength), in which we firstly calculate

Methods

Results

Conclusion