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The link-prediction problem for social networks
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Learning spectral graph transformations for link prediction
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Grocery shopping recommendations based on basket-sensitive random walk
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
New perspectives and methods in link prediction
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Supervised random walks: predicting and recommending links in social networks
Proceedings of the fourth ACM international conference on Web search and data mining
Feature selection for link prediction
Proceedings of the 5th Ph.D. workshop on Information and knowledge
Learning latent friendship propagation networks with interest awareness for link prediction
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Co-occurrence prediction in a large location-based social network
Frontiers of Computer Science: Selected Publications from Chinese Universities
Who proposed the relationship?: recovering the hidden directions of undirected social networks
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Link prediction is a fundamental problem in social network analysis. The key technique in unsupervised link prediction is to find an appropriate similarity measure between nodes of a network. A class of wildly used similarity measures are based on random walk on graph. The traditional random walk (TRW) considers the link structures by treating all nodes in a network equivalently, and ignores the centrality of nodes of a network. However, in many real networks, nodes of a network not only prefer to link to the similar node, but also prefer to link to the central nodes of the network. To address this issue, we use maximal entropy random walk (MERW) for link prediction, which incorporates the centrality of nodes of the network. First, we study certain important properties of MERW on graph $G$ by constructing an eigen-weighted graph G. We show that the transition matrix and stationary distribution of MERW on G are identical to the ones of TRW on G. Based on G, we further give the maximal entropy graph Laplacians, and show how to fast compute the hitting time and commute time of MERW. Second, we propose four new graph kernels and two similarity measures based on MERW for link prediction. Finally, to exhibit the power of MERW in link prediction, we compare 27 various link prediction methods over 3 synthetic and 8 real networks. The results show that our newly proposed MERW based methods outperform the state-of-the-art method on most datasets.