2015 EfficientLatentLinkRecommendati

• (Song et al., 2015) ⇒ Dongjin Song, David A. Meyer, and Dacheng Tao. (2015). “Efficient Latent Link Recommendation in Signed 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:10.1145/2783258.2783358

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• http://dl.acm.org/citation.cfm?id=2783258.2783358&preflayout=flat#citedby

Quotes

Author Keywords

• Gauc; information filtering; link recommendation; recommender systems; signed networks

Abstract

Signed networks, in which the relationship between two nodes can be either positive (indicating a relationship such as trust) or negative (indicating a relationship such as distrust), are becoming increasingly common. A plausible model for user behavior analytics in signed networks can be based upon the assumption that more extreme positive and negative relationships are explored and exploited before less extreme ones. Such a model implies that a personalized ranking list of latent links should place positive links on the top, negative links at the bottom, and unknown status links in between. Traditional ranking metrics, e.g., area under the receiver operating characteristic curve (AUC), are however not suitable for quantifying such a ranking list which includes positive, negative, and unknown status links. To address this issue, a generalized AUC (GAUC) which can measure both the head and tail of a ranking list has been introduced. Since GAUC weights each pairwise comparison equally and the calculation of GAUC requires quadratic time, we derive two lower bounds of GAUC which can be computed in linear time and put more emphasis on ranking positive links on the top and negative links at the bottom of a ranking list. Next, we develop two efficient latent link recommendation (ELLR) algorithms in order to recommend links by directly optimizing these two lower bounds, respectively. Finally, we compare these two ELLR algorithms with top-performing baseline methods over four benchmark datasets, among which the largest network has more than 100 thousand nodes and seven million entries. Thorough empirical studies demonstrate that the proposed ELLR algorithms outperform state-of-the-art approaches for link recommendation in signed networks at no cost in efficiency.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 EfficientLatentLinkRecommendati	Dacheng Tao Dongjin Song David A. Meyer			Efficient Latent Link Recommendation in Signed Networks				10.1145/2783258.2783358		2015