2013 SocialInfluencebasedClusteringo

• (Zhou & Liu, 2013) ⇒ Yang Zhou, and Ling Liu. (2013). “Social Influence based Clustering of Heterogeneous Information Networks.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487640

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Social+Influence+based+Clustering+of+Heterogeneous+Information+Networks
• http://dl.acm.org/citation.cfm?id=2487575.2487640&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; graph clustering; heterogeneous network; social influence

Abstract

Social networks continue to grow in size and the type of information hosted. We witness a growing interest in clustering a social network of people based on both their social relationships and their participations in activity based information networks. In this paper, we present a social influence based clustering framework for analyzing heterogeneous information networks with three unique features. First, we introduce a novel social influence based vertex similarity metric in terms of both self-influence similarity and co-influence similarity. We compute self-influence and co-influence based similarity based on social graph and its associated activity graphs and influence graphs respectively. Second, we compute the [[combined social influence based similarity]] between each pair of vertices by unifying the self-similarity and multiple co-influence similarity scores through a weight function with an iterative update method. Third, we design an iterative learning algorithm, SI-Cluster, to dynamically refine the K clusters by continuously quantifying and adjusting the weights on self-influence similarity and on multiple co-influence similarity scores towards the clustering convergence. To make SI-Cluster converge fast, we transformed a sophisticated nonlinear fractional programming problem of multiple weights into a straightforward nonlinear parametric programming problem of single variable. Our experiment results show that SI-Cluster not only achieves a better balance between self-influence and co-influence similarities but also scales extremely well for large graph clustering.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 SocialInfluencebasedClusteringo	Ling Liu Yang Zhou			Social Influence based Clustering of Heterogeneous Information Networks				10.1145/2487575.2487640		2013