2007 LocalProbModelsForLinkPrediction

• (Wang et al., 2007) ⇒ Chao Wang, Venu Satuluri, Srinivasan Parthasarathy. (2007). “Local Probabilistic Models for Link Prediction.” In: Proceedings of Seventh IEEE International Conference on Data Mining (ICDM 2007). [doi:10.1109/ICDM.2007.108]

Subject Headings: Link Prediction Algorithm

Cited By

~7 http://scholar.google.com/scholar?cites=15698884345362385756

Quotes

Abtract

• One of the core tasks in social network analysis is to predict the formation of links (i.e. various types of relationships) over time. Previous research has generally represented the social network in the form of a graph and has leveraged topological and semantic measures of similarity between two nodes to evaluate the probability of link formation. Here we introduce a novel local probabilistic graphical model method that can scale to large graphs to estimate the joint co-occurrence probability of two nodes. Such a probability measure captures information that is not captured by either topological measures or measures of semantic similarity, which are the dominant measures used for link prediction. We demonstrate the effectiveness of the co-occurrence probability feature by using it both in isolation and in combination with other topological and semantic features for predicting co-authorship collaborations on three real datasets.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2007 LocalProbModelsForLinkPrediction	Srinivasan Parthasarathy Chao Wang Venu Satuluri			Local Probabilistic Models for Link Prediction		Proceedings of Seventh IEEE International Conference on Data Mining	http://www.cse.ohio-state.edu/~satuluri/ICDM2007.pdf	10.1109/ICDM.2007.108		2007