Modeling the invisible college
Journal of the American Society for Information Science and Technology
Link Prediction of Social Networks Based on Weighted Proximity Measures
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Scalable learning of collective behavior based on sparse social dimensions
Proceedings of the 18th ACM conference on Information and knowledge management
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
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Traditional link prediction techniques primarily focus on the effect of potential linkages on the local network neighborhood or the paths between nodes. In this paper, we study the problem of link prediction in networks where instances can simultaneously belong to multiple communities, engendering different types of collaborations. Links in these networks arise from heterogeneous causes, limiting the performance of predictors that treat all links homogeneously. To solve this problem, we introduce a new link prediction framework, Link Prediction using Social Features (LPSF), which weights the network using a similarity function based on features extracted from patterns of prominent interactions across the network.