2014 ActivityEdgeCentricMultiLabelCl

• (Zhou & Liu, 2014) ⇒ Yang Zhou, and Ling Liu. (2014). “Activity-edge Centric Multi-label Classification for Mining Heterogeneous Information Networks.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623737

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Activity-edge+Centric+Multi-label+Classification+for+Mining+Heterogeneous+Information+Networks
• http://dl.acm.org/citation.cfm?id=2623330.2623737&preflayout=flat#citedby

Quotes

Author Keywords

• Activity-based edge classification; collaboration multigraph; data mining; heterogeneous network; label vicinity; multi-label classification

Abstract

Multi-label classification of heterogeneous information networks has received renewed attention in social network analysis. In this paper, we present an activity-edge centric multi-label classification framework for analyzing heterogeneous information networks with three unique features. First, we model a heterogeneous information network in terms of a collaboration graph and multiple associated activity graphs. We introduce a novel concept of vertex-edge homophily in terms of both vertex labels and edge labels and transform a general collaboration graph into an activity-based collaboration multigraph by augmenting its edges with class labels from each activity graph through activity-based edge classification. Second, we utilize the label vicinity to capture the pairwise vertex closeness based on the labeling on the activity-based collaboration multigraph. We incorporate both the structure affinity and the label vicinity into a unified classifier to speed up the classification convergence. Third, we design an iterative learning algorithm, AEClass, to dynamically refine the classification result by continuously adjusting the weights on different activity-based edge classification schemes from multiple activity graphs, while constantly learning the contribution of the structure affinity and the label vicinity in the unified classifier. Extensive evaluation on real datasets demonstrates that AEClass outperforms existing representative methods in terms of both effectiveness and efficiency.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 ActivityEdgeCentricMultiLabelCl	Ling Liu Yang Zhou			Activity-edge Centric Multi-label Classification for Mining Heterogeneous Information Networks				10.1145/2623330.2623737		2014