2013 LinkPredictionwithSocialVectorC

• (Lee et al., 2013) ⇒ Conrad Lee, Bobo Nick, Ulrik Brandes, and Pádraig Cunningham. (2013). “Link Prediction with Social Vector Clocks.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487615

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Link+Prediction+with+Social+Vector+Clocks
• http://dl.acm.org/citation.cfm?id=2487575.2487615&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; link prediction; online algorithms; social networks; vector clocks

Abstract

State-of-the-art link prediction utilizes combinations of complex features derived from network panel data. We here show that computationally less expensive features can achieve the same performance in the common scenario in which the data is available as a sequence of interactions. Our features are based on social vector clocks, an adaptation of the vector-clock concept introduced in distributed computing to social interaction networks. In fact, our experiments suggest that by taking into account the order and spacing of interactions, social vector clocks exploit different aspects of link formation so that their combination with previous approaches yields the most accurate predictor to date.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 LinkPredictionwithSocialVectorC	Conrad Lee Bobo Nick Ulrik Brandes Pádraig Cunningham			Link Prediction with Social Vector Clocks				10.1145/2487575.2487615		2013