The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Link Prediction of Social Networks Based on Weighted Proximity Measures
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
Using ghost edges for classification in sparsely labeled networks
Proceedings of the 14th 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
Supervised random walks: predicting and recommending links in social networks
Proceedings of the fourth ACM international conference on Web search and data mining
A link prediction approach to recommendations in large-scale user-generated content systems
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
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Link prediction in weighted network is an important task in Social Network Analysis. This problem aims at determining missing links in weighted networks. By taking advantage of the weights and structural information of networks, a mechanism for rating nodes' authorities in terms of the value of weight, called Benefit Rank, is defined. This mechanism can flexibly collect different order neighbors' information of nodes to complete the rating authority process for each node in weighted networks. Using Benefit Rank combined with the Weak Ties theory, similarity measures are proposed to estimate the emergence of future relationships between nodes in weighted networks. Extensive experiments were carried out on four real weighted networks. Compared with existing methods, our methods can provide higher accuracy for link prediction in weighted networks.