Vertex collocation profiles: subgraph counting for link analysis and prediction
Proceedings of the 21st international conference on World Wide Web
ALIVE: a multi-relational link prediction environment for the healthcare domain
PAKDD'12 Proceedings of the 2012 Pacific-Asia conference on Emerging Trends in Knowledge Discovery and Data Mining
One-mode Projection of Multiplex Bipartite Graphs
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Social Recommendation Based on Multi-relational Analysis
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Unsupervised link prediction using aggregative statistics on heterogeneous social networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Social Link Prediction in Online Social Tagging Systems
ACM Transactions on Information Systems (TOIS)
Learning latent representations of nodes for classifying in heterogeneous social networks
Proceedings of the 7th ACM international conference on Web search and data mining
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Many important real-world systems, modeled naturally as complex networks, have heterogeneous interactions and complicated dependency structures. Link prediction in such networks must model the influences between heterogenous relationships and distinguish the formation mechanisms of each link type, a task which is beyond the simple topological features commonly used to score potential links. In this paper, we introduce a novel probabilistically weighted extension of the Adamic/Adar measure for heterogenous information networks, which we use to demonstrate the potential benefits of diverse evidence, particularly in cases where homogeneous relationships are very sparse. However, we also expose some fundamental flaws of traditional a priori link prediction. In accordance with previous research on homogeneous networks, we further demonstrate that a supervised approach to link prediction can enhance performance and is easily extended to the heterogeneous case. Finally, we present results on three diverse, real-world heterogeneous information networks and discuss the trends and tradeoffs of supervised and unsupervised link prediction in a multi-relational setting.