Identifying Evolving Groups in Dynamic Multimode Networks

Lei Tang; Huan Liu; Jianping Zhang

doi:10.1109/TKDE.2011.159

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2012
Issue No. 1 - January
Abstract - Identifying Evolving Groups in Dynamic Multimode Networks

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Identifying Evolving Groups in Dynamic Multimode Networks

January 2012 (vol. 24 no. 1)

pp. 72-85

	ASCII Text	x
	Lei Tang, Huan Liu, Jianping Zhang, "Identifying Evolving Groups in Dynamic Multimode Networks," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 1, pp. 72-85, January, 2012.

	BibTex	x
	@article{ 10.1109/TKDE.2011.159, author = {Lei Tang and Huan Liu and Jianping Zhang}, title = {Identifying Evolving Groups in Dynamic Multimode Networks}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {24}, number = {1}, issn = {1041-4347}, year = {2012}, pages = {72-85}, doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.159}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - JOUR JO - IEEE Transactions on Knowledge and Data Engineering TI - Identifying Evolving Groups in Dynamic Multimode Networks IS - 1 SN - 1041-4347 SP72 EP85 EPD - 72-85 A1 - Lei Tang, A1 - Huan Liu, A1 - Jianping Zhang, PY - 2012 KW - Data mining KW - community detection KW - community evolution KW - multimode networks KW - dynamic networks. VL - 24 JA - IEEE Transactions on Knowledge and Data Engineering ER -

Lei Tang, Arizona State University, Tempe

Huan Liu, Arizona State University, Tempe

Jianping Zhang, The MITRE Corporation, McLean

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.159

A multimode network consists of heterogeneous types of actors with various interactions occurring between them. Identifying communities in a multimode network can help understand the structural properties of the network, address the data shortage and unbalanced problems, and assist tasks like targeted marketing and finding influential actors within or between groups. In general, a network and its group structure often evolve unevenly. In a dynamic multimode network, both group membership and interactions can evolve, posing a challenging problem of identifying these evolving communities. In this work, we try to address this problem by employing the temporal information to analyze a multimode network. A temporally regularized framework and its convergence property are carefully studied. We show that the algorithm can be interpreted as an iterative latent semantic analysis process, which allows for extensions to handle networks with actor attributes and within-mode interactions. Experiments on both synthetic data and real-world networks demonstrate the efficacy of our approach and suggest its generality in capturing evolving groups in networks with heterogeneous entities and complex relationships.

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Index Terms:

Data mining, community detection, community evolution, multimode networks, dynamic networks.

Citation:

Lei Tang, Huan Liu, Jianping Zhang, "Identifying Evolving Groups in Dynamic Multimode Networks," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 1, pp. 72-85, Jan. 2012, doi:10.1109/TKDE.2011.159

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