A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection

Jing Liu,Qi Li,Junfang Guo,Siew Ann Cheong

doi:10.1155/2021/7428927

Abstract

Community structure is one of the most important characteristics of complex networks, which has important applications in sociology, biology, and computer science. The community detection method based on local expansion is one of the most adaptable overlapping community detection algorithms. However, due to the lack of effective seed selection and community optimization methods, the algorithm often gets community results with lower accuracy. In order to solve these problems, we propose a seed selection algorithm of fusion degree and clustering coefficient. The method calculates the weight value corresponding to degree and clustering coefficient by entropy weight method and then calculates the weight factor of nodes as the seed node selection order. Based on the seed selection algorithm, we design a local expansion strategy, which uses the strategy of optimizing adaptive function to expand the community. Finally, community merging and isolated node adjustment strategies are adopted to obtain the final community. Experimental results show that the proposed algorithm can achieve better community partitioning results than other state-of-the-art algorithms.

Highlights

Complex networks are ubiquitous in the real world, such as social networks, academic cooperation networks, world wide networks, and biological networks [1]. ey are generally composed of nodes and edges
This paper proposes an overlapping community detection algorithm based on information fusion. e main contributions of this paper are as follows: (i) We propose an overlapping community detection algorithm based on information fusion, which improves the quality of community detection through an effective seed selection method and community optimization methods
In order to solve these problems, we propose a seed selection method based on the importance of nodes based on the degree of fusion and clustering coefficient, which ensures that the seeds have a large total node influence and ensures that the internal nodes of the seeds have a high degree of similarity

Summary

Introduction

Complex networks are ubiquitous in the real world, such as social networks, academic cooperation networks, world wide networks, and biological networks [1]. ey are generally composed of nodes (individuals) and edges (relationships between individuals). In social networks, nodes represent people and edges represent relationships between people. These networks belong to different fields, they follow the same laws. Community structure is one of the most important structural features of complex networks [2]. It is ubiquitous in various complex networks in the real world. In social networks, individuals with common interests have closer relationships and form communities of common interests. In the academic cooperation network, scholars who have similar research directions or have participated in similar projects constitute a community. Community detection technology can help cross-research projects between statistics departments

Methods

Results

Conclusion