2012 IntegratingMetaPathSelectionwit

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Notes

Real-world, multiple-typed objects are often interconnected, forming heterogeneous information networks. A major challenge for link-based clustering in such networks is its potential to generate many different results, carrying rather diverse semantic meanings. In order to generate desired clustering, we propose to use meta-path, a path that connects object types via a sequence of relations, to control clustering with distinct semantics. Nevertheless, it is easier for a user to provide a few examples ("seeds") than a weighted combination of sophisticated meta-paths to specify her clustering preference. Thus, we propose to integrate meta-path selection with user-guided clustering to cluster objects in networks, where a user first provides a small set of object seeds for each cluster as guidance. Then the system learns the weights for each meta-path that are consistent with the clustering result implied by the guidance, and generates clusters under the learned weights of meta-paths. A probabilistic approach is proposed to solve the problem, and an effective and efficient iterative algorithm, PathSelClus, is proposed to learn the model, where the clustering quality and the meta-path weights are mutually enhancing each other. Our experiments with several clustering tasks in two real networks demonstrate the power of the algorithm in comparison with the baselines.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 IntegratingMetaPathSelectionwit	Yizhou Sun Philip S. Yu Xifeng Yan Xiao Yu Brandon Norick Jiawei Han			Integrating Meta-path Selection with User-guided Object Clustering in Heterogeneous Information Networks				10.1145/2339530.2339738		2012