You are who you know: inferring user profiles in online social networks
Proceedings of the third ACM international conference on Web search and data mining
Organizational overlap on social networks and its applications
Proceedings of the 22nd international conference on World Wide Web
Proceedings of the 7th ACM international conference on Web search and data mining
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Because network data is often incomplete, researchers consider the link prediction problem, which asks which non-existent edges in an incomplete network are most likely to exist in the complete network. Classical approaches compute the 'similarity' of two nodes, and conclude that highly similar nodes are most likely to be connected in the complete network. Here, we consider several such similarity-based measures, but supplement the similarity calculations with community information. We show that for many networks, the inclusion of community information improves the accuracy of similarity-based link prediction methods.