2014 MetaPathbasedMultiNetworkCollec

• (Zhang et al., 2014) ⇒ Jiawei Zhang, Philip S. Yu, and Zhi-Hua Zhou. (2014). “Meta-path based Multi-network Collective Link Prediction.” 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:10.1145/2623330.2623645

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Cited By

• http://scholar.google.com/scholar?q=%222014%22+Meta-path+based+Multi-network+Collective+Link+Prediction
• http://dl.acm.org/citation.cfm?id=2623330.2623645&preflayout=flat#citedby

Quotes

Author Keywords

• Classification; data mining; data mining; link prediction; transfer learning

Abstract

Online social networks offering various services have become ubiquitous in our daily life. Meanwhile, users nowadays are usually involved in multiple online social networks simultaneously to enjoy specific services provided by different networks. Formally, social networks that share some common users are named as partially aligned networks. In this paper, we want to predict the formation of social links in multiple partially aligned social networks at the same time, which is formally defined as the multi-network link (formation) prediction problem. In multiple partially aligned social networks, users can be extensively correlated with each other by various connections. To categorize these diverse connections among users, 7 “intra-network social meta paths " and 4 categories of “inter-network social meta paths " are proposed in this paper. These “social meta paths " can cover a wide variety of connection information in the network, some of which can be helpful for solving the multi-network link prediction problem but some can be not. To utilize useful connection, a subset of the most informative “social meta paths " are picked, the process of which is formally defined as “social meta path selection” in this paper. An effective general link formation prediction framework, Mli (Multi-network Link Identifier), is proposed in this paper to solve the multi-network link (formation) prediction problem. Built with heterogenous topological features extracted based on the selected “social meta paths " in the multiple partially aligned social networks, Mli can help refine and disambiguate the prediction results reciprocally in all aligned networks. Extensive experiments conducted on real-world partially aligned heterogeneous networks, Foursquare and Twitter, demonstrate that Mli can solve the multi-network link prediction problem very well.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 MetaPathbasedMultiNetworkCollec	Zhi-Hua Zhou Philip S. Yu Jiawei Zhang			Meta-path based Multi-network Collective Link Prediction				10.1145/2623330.2623645		2014