Community Detection Based on Density Peak Clustering Model and Multiple Attribute Decision-Making Strategy TOPSIS

Jianjun Cheng,Xu Wang,Jun Li,Nuo Chen,Wenshuang Gong,Xiaoyun Chen,Giacomo Fiumara

doi:10.1155/2021/1772407

Jianjun Cheng, Xu Wang + Show 5 more

Open Access

https://doi.org/10.1155/2021/1772407

Copy DOI

Journal: Complexity	Publication Date: Dec 7, 2021
Citations: 5	License type: CC BY 4.0

Affiliation: Lanzhou University

Abstract

Community detection is one of the key research directions in complex network studies. We propose a community detection algorithm based on a density peak clustering model and multiple attribute decision-making strategy, TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution). First, the two-dimensional dataset, which is transformed from the network by taking the density and distance as the attributes of nodes, is clustered by using the DBSCAN algorithm, and outliers are determined and taken as the key nodes. Then, the initial community frameworks are formed and expanded by adding the most similar node of the community as its new member. In this process, we use TOPSIS to cohesively integrate four kinds of similarities to calculate an index, and use it as a criterion to select the most similar node. Then, we allocate the nonkey nodes that are not covered in the expanded communities. Finally, some communities are merged to obtain a stable partition in two ways. This paper designs some experiments for the algorithm on some real networks and some synthetic networks, and the proposed method is compared with some popular algorithms. The experimental results testify for the effectiveness and show the accuracy of our algorithm.

Highlights

Research on complex networks [1] has been an important aspect of data mining
Researches show that the networks abstracted from the real systems often have such characteristics as small-world [2], scale-free [3], and community structure [4, 5]. e smallworld characteristic shows that the nodes in the network are connected by a short path; and the scale-free feature means that the degree of the nodes follows a power-law distribution
E nodes in the network can be divided into several groups, wherein the nodes within each group have more dense connections, and the connections between the groups sparse, with each group constituting a so-called “community.” e community structure contains the organizational information in each part of the network and the interaction information between these parts, which can be of great help to the research on the underlying structure and potential functions of the actual systems

Summary

Research Article

We propose a community detection algorithm based on a density peak clustering model and multiple attribute decision-making strategy, TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution). En, the initial community frameworks are formed and expanded by adding the most similar node of the community as its new member. The two-dimensional dataset, which is transformed from the network by taking the density and distance as the attributes of nodes, is clustered by using the DBSCAN algorithm, and outliers are determined and taken as the key nodes. In this process, we use TOPSIS to cohesively integrate four kinds of similarities to calculate an index, and use it as a criterion to select the most similar node.

Introduction

Experiments

Eps number of center

PPC Infomap FastQ football

FastQ Attractor Walktrap Louvain Infomap