Uncovering Hidden Community Structure in Multi-Layer Networks

Faiza Riaz Khawaja,Jinfang Sheng,Bin Wang,Yumna Memon

doi:10.3390/app11062857

Faiza Riaz Khawaja, Jinfang Sheng + Show 2 more

Open Access

https://doi.org/10.3390/app11062857

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Journal: Applied sciences	Publication Date: Mar 23, 2021
Citations: 10	License type: CC BY 4.0

Affiliation: Central South University

Abstract

Community detection, also known as graph clustering, in multi-layer networks has been extensively studied in the literature. The goal of community detection is to partition vertices in a network into densely connected components so called communities. Networks contain a set of strong, dominant communities, which may interfere with the detection of weak, natural community structure. When most of the members of the weak communities also belong to stronger communities, they are extremely hard to be uncovered. We call the weak communities the hidden or disguised community structure. In this paper, we present a method to uncover weak communities in a network by weakening the strength of the dominant structure. With the aim to detect the weak communities, through experiments, we observe real-world networks to answer the question of whether real-world networks have hidden community structure or not. Results of the hidden community detection (HCD) method showed the great variation in the number of communities detected in multiple layers when compared with the results of other community detection methods.

Highlights

The structure of the rest of the paper is as follows: in Section 2, we discuss some details about the related work from the proposed work in this paper; in Section 3, we discuss the preliminaries and definitions related to our work; in Section 4, we introduce the hidden community detection method in detail and talk about the steps of its operation; in Sections 5 and 6, we discuss the experiments; and evaluate the results of HCD method with several real-world datasets; later in Section 7, we compare our method with some multi-layer and other hidden community detection approaches developed so far, in terms of some predefined attributes; lastly, we end this paper with the conclusion
Based getting theComparison above results from experimenting with the HCD algorithm on different real-world network datasets, we compared the results with two algorithms: we compare some multi-layer community detection algorithms with (1)
A community detection algorithm can fail in many different ways; for example, it may only be able to detect a subset of the desired community structure, or it may detect all of the desired communities and several spurious communities

Summary

Introduction

Uncovering community structures of a complex network can help us to understand how the network functions. On the other hand, finding communities and attempting to analyze them is a compelling approach to understand all kinds of network organization structures and their functions. These communities often correspond to some functional units. The problem of finding community has received significant research attention in the field of network analysis, as it can reveal information about the network structure and the flow of information throughout the network [1]. One critical issue, which has been extensively studied in the literature since the early analysis of complex networks, is the identification of communities or relationships hidden within the structure of these networks.

Methods

Results

Conclusion