2015 CoupledLPLinkPredictioninCouple

• (Dong et al., 2015) ⇒ Yuxiao Dong, Jing Zhang, Jie Tang, Nitesh V. Chawla, and Bai Wang. (2015). “CoupledLP: Link Prediction in Coupled Networks.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783329

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Cited By

• http://scholar.google.com/scholar?q=%222015%22+CoupledLP%3A+Link+Prediction+in+Coupled+Networks
• http://dl.acm.org/citation.cfm?id=2783258.2783329&preflayout=flat#citedby

Quotes

Author Keywords

• Coupled networks; data mining; healthcare; link prediction; mobile communication networks; social networks; sociology

Abstract

We study the problem of link prediction in coupled networks, where we have the structure information of one (source) network and the interactions between this network and another (target) network. The goal is to predict the missing links in the target network. The problem is extremely challenging as we do not have any information of the target network. Moreover, the source and target networks are usually heterogeneous and have different types of nodes and links. How to utilize the structure information in the source network for predicting links in the target network? How to leverage the heterogeneous interactions between the two networks for the prediction task?

We propose a unified framework, CoupledLP, to solve the problem. Given two coupled networks, we first leverage atomic propagation rules to automatically construct implicit links in the target network for addressing the challenge of target network incompleteness, and then propose a coupled factor graph model to incorporate the meta-paths extracted from the coupled part of the two networks for transferring heterogeneous knowledge. We evaluate the proposed framework on two different genres of datasets: disease-gene (DG) and mobile social networks. In the DG networks, we aim to use the disease network to predict the associations between genes. In the mobile networks, we aim to use the mobile communication network of one mobile operator to infer the network structure of its competitors. On both datasets, the proposed CoupledLP framework outperforms several alternative methods. The proposed problem of coupled link prediction and the corresponding framework demonstrate both the scientific and business applications in biology and social networks.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 CoupledLPLinkPredictioninCouple	Bai Wang Jie Tang Jing Zhang Nitesh V. Chawla Yuxiao Dong			CoupledLP: Link Prediction in Coupled Networks				10.1145/2783258.2783329		2015