Computer Science > Social and Information Networks
[Submitted on 25 May 2016 (v1), last revised 27 Feb 2017 (this version, v3)]
Title:Temporal Clustering in Dynamic Networks with Tensor Decomposition
View PDFAbstract:Dynamic networks are increasingly being usedd to model real world datasets. A challenging task in their analysis is to detect and characterize clusters. It is useful for analyzing real-world data such as detecting evolving communities in networks. We propose a temporal clustering framework based on a set of network generative models to address this problem. We use PARAFAC decomposition to learn network models from this http URL then use $K$-means for clustering, the Silhouette criterion to determine the number of clusters, and a similarity score to order the clusters and retain the significant ones. In order to address the time-dependent aspect of these clusters, we propose a segmentation algorithm to detect their formations, dissolutions and lifetimes. Synthetic networks with ground truth and real-world datasets are used to test our method against state-of-the-art, and the results show that our method has better performance in clustering and lifetime detection than previous methods.
Submission history
From: Kun Tu [view email][v1] Wed, 25 May 2016 21:07:14 UTC (537 KB)
[v2] Mon, 20 Feb 2017 06:18:34 UTC (803 KB)
[v3] Mon, 27 Feb 2017 05:54:43 UTC (1,210 KB)
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