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Mobile Networks and Applications
Erschienen in: Mobile Networks and Applications 1/2018

30.05.2017

Community Detection Based on Regularized Semi-Nonnegative Matrix Tri-Factorization in Signed Networks

verfasst von: Zhen Li, Jian Chen, Ying Fu, Guyu Hu, Zhisong Pan, Liangliang Zhang

Erschienen in: Mobile Networks and Applications | Ausgabe 1/2018

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Abstract

Community detection is a fundamental task in the social network analysis field, which is beneficial for many real-world applications such as recommendation systems and telephone fraud detection. Community detection in unsigned networks has been extensively studied, however, few works focus on community detection in signed networks. Under this background, we propose a framework based on regularized semi-nonnegative matrix tri-factorization which maps the signed network from high-dimensional space to low-dimensional space, such that the communities of the signed network can be derived. In addition, to improve the detection accuracy, we introduce a graph regularization to distribute the pair of nodes which are connected with negative links into different communities. The experimental results on both synthetic datasets and real-world datasets verify the effectiveness of the proposed method.
Vorheriger Artikel Editorial: Machine Learning and Intelligent Communications
Nächster Artikel Physical-Layer Network Coding Scheme over Asymmetric Rayleigh Fading Two-Way Relay Channels

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Metadaten
Titel
Community Detection Based on Regularized Semi-Nonnegative Matrix Tri-Factorization in Signed Networks
verfasst von
Zhen Li
Jian Chen
Ying Fu
Guyu Hu
Zhisong Pan
Liangliang Zhang
Publikationsdatum
30.05.2017
Verlag
Springer US
Erschienen in
Mobile Networks and Applications / Ausgabe 1/2018
Print ISSN: 1383-469X
Elektronische ISSN: 1572-8153
DOI
https://doi.org/10.1007/s11036-017-0883-0

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