<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Traditional community detection algorithms are easily interfered by noises and outliers. Therefore, we propose to leverage a clustering fusion method to improve the results of community detection. Usually, there are two issues in clustering ensembles: how to generate efficient diversified cluster members, and how to ensembles the results of all members. Specifically: (1) considering the time evolving characteristic of real world networks, we propose to generate clustering members based on the snapshot of networks, where the split based clustering algorithms are performed; (2) considering the difference in the distribution of the cluster centers in each clustering member and the actual distribution, we ensemble the results based on a maximum likelihood method. Moreover, we conduct experiments to show that our method can discover high quality communities.</p></abstract>

Traditional community detection algorithms are easily interfered by noises and outliers. Therefore, we propose to leverage a clustering fusion method to improve the results of community detection. Usually, there are two issues in clustering ensembles: how to generate efficient diversified cluster members, and how to ensembles the results of all members. Specifically: (1) considering the time evolving characteristic of real world networks, we propose to generate clustering members based on the snapshot of networks, where the split based clustering algorithms are performed; (2) considering the difference in the distribution of the cluster centers in each clustering member and the actual distribution, we ensemble the results based on a maximum likelihood method. Moreover, we conduct experiments to show that our method can discover high quality communities.

Improving Community Detection in Time-Evolving Networks Through Clustering Fusion

School of Information Science and Technology, Donghua University, Shanghai, 201620 China

School of Computer Science and Information Technology, Zhejiang Wanli University, Ningbo, 315100 China

School of Science, Donghua University, Shanghai, 201620 China

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Traditional community detection algorithms are easily interfered by noises and outliers. Therefore, we propose to leverage a clustering fusion method to...

Improving Community Detection in Time-Evolving Networks Through Clustering Fusion

Publié en ligne: 03 juil. 2015

Pages: 63 - 74

DOI: https://doi.org/10.1515/cait-2015-0029

© by Ran Jin

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.