|Table of Contents|

A Unified Semi-supervised Community Detection Approach Integrating Network Topology and Node Contents(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2023年01期
Page:
130-138
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
A Unified Semi-supervised Community Detection Approach Integrating Network Topology and Node Contents
Author(s):
Xu Weizhong1; Cao Jinxin1; Jin Di2; Sun Xiang3; Zhang Xiaofeng1; Liu Lu3; Ding Weiping1
(1.School of Information Science and Technology, Nantong University, Nantong 226019, China)
(2.College of Intelligence and Computing, Tianjin University, Tianjin 300350, China)
(3.School of Informatics, University of Leicester, Leicester, UK LE1 7RH)
Keywords:
community detection;  node contents;  prior information;  stochastic block;  nonnegative matrix factorization
PACS:
TP182
DOI:
10.3969/j.issn.1001-4616.2023.01.017
Abstract:
Community detection plays an increasing important role in complex network analysis. There is still a goal that how to improve the performance of community detection in real applications. Due to the content in networks helpful to identifying communities,some methods focus on combining network topology with node content,which obtains no bad performance of community detection. Besides,some community detection enhancement methods are mainly based on designing the topological similarity of nodes to adjust network topology,which aims to achieve the enhancement. In order to further improve the quality of community detection,we propose a unified method,Semi-supervised Community Detection with Node Contents,shorted as SCDNC,which not only apply the enhancement into community detection,but also achieve the integration of network topology and node content. In the new method,firstly,we propose a stochastic block model to describe the community memberships of nodes. Secondly,we present another stochastic model to describe the community memberships of node contents,which utilizes community memberships of nodes as weight vectors of node contents. By now,integrating network topology with node content is achieved. Thirdly,we calculate the topological similarity of nodes by using links,and then model the prior information based on topological similarity,i.e.,we make nodes and their most similar neighbors have the same community membership. Finally,we present a nonnegative matrix factorization approach to obtain the parameters of the model. On both synthetic and real-world networks with ground-truths,we compare performance of the new method with the state-of-the-art methods. The experimental results show that the new method obtains significant improvement for community detection via combining node contents and network topology enhanced by prior information.

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Last Update: 2023-03-15