An Overlapping Community Detection Approach in Ego-Splitting Networks Using Symmetric Nonnegative Matrix Factorization

Mingqing Huang,Qiang Qu,Abdur Rasool,Qingshan Jiang

doi:10.3390/sym13050869

Abstract

Overlapping clustering is a fundamental and widely studied subject that identifies all densely connected groups of vertices and separates them from other vertices in complex networks. However, most conventional algorithms extract modules directly from the whole large-scale graph using various heuristics, resulting in either high time consumption or low accuracy. To address this issue, we develop an overlapping community detection approach in Ego-Splitting networks using symmetric Nonnegative Matrix Factorization (ESNMF). It primarily divides the whole network into many sub-graphs under the premise of preserving the clustering property, then extracts the well-connected sub-sub-graph round each community seed as prior information to supplement symmetric adjacent matrix, and finally identifies precise communities via nonnegative matrix factorization in each sub-network. Experiments on both synthetic and real-world networks of publicly available datasets demonstrate that the proposed approach outperforms the state-of-the-art methods for community detection in large-scale networks.

Highlights

Since the ground-breaking advent of online social networking, complex network analysis tools have been developed in the last decades for excerpting insights from the various relationships between participants [1]
As for the reason why the Ego-Splitting networks using symmetric Nonnegative Matrix Factorization (ESNMF) scheme shows a more outstanding effectiveness on community modularity but there are little differences on Normalized Mutual Information (NMI) between all involved methods, we argue that one of the most important causes is the probabilistic dependency of the generating procedure for LF-SW and LF-LW networks, which can be avoid in actual networks
As for the efficiency comparison, despite the fact that our framework consumes the longest time compared with the other schemes as illustrated in Figure 4, these differences are not very significant, diverging in the same order of magnitude, which means that the ESNMF method can satisfy the practical requirements

Summary

Introduction

Since the ground-breaking advent of online social networking, complex network analysis tools have been developed in the last decades for excerpting insights from the various relationships between participants [1]. Network analysis has become a research hotspot to uncover critical patterns that facilitate the understanding of phenomena for a variety of applications. Communities indicate similar opinions, functions, objectives, etc., which are ubiquitously and naturally present as basic modules in real-world networks. Community detection is a fundamental problem in complex networks, consisting of the unsupervised division of elements into densely knitted and highly related clusters, where the connectivity between different groups is relatively loose. Revealing the clustering structure of realworld networks has emerged as a basic protocol in many data mining tasks, such as human seizure tracking [5], society influence maximization [6], cancer tissue phenotyping [7], and semantic trajectory clustering [8]. Research on the topology of real-world networks and their modular structure is at the core of network analysis

Methods

Results

Discussion

Conclusion