Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
PageRank, HITS and a unified framework for link analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Link fusion: a unified link analysis framework for multi-type interrelated data objects
Proceedings of the 13th international conference on World Wide Web
CubeSVD: a novel approach to personalized Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Object-level ranking: bringing order to Web objects
WWW '05 Proceedings of the 14th international conference on World Wide Web
Higher-Order Web Link Analysis Using Multilinear Algebra
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Beyond streams and graphs: dynamic tensor analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Probability Models, Ninth Edition
Introduction to Probability Models, Ninth Edition
Markov Chains: Models, Algorithms and Applications (International Series in Operations Research & Management Science)
Incremental tensor analysis: Theory and applications
ACM Transactions on Knowledge Discovery from Data (TKDD)
Co-ranking Authors and Documents in a Heterogeneous Network
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
A generalized Co-HITS algorithm and its application to bipartite graphs
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
MetaFac: community discovery via relational hypergraph factorization
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling and multiway analysis of chatroom tensors
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
Towards an effective and unbiased ranking of scientific literature through mutual reinforcement
Proceedings of the 21st ACM international conference on Information and knowledge management
"You know because I know": a multidimensional network approach to human resources problem
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Reduce and aggregate: similarity ranking in multi-categorical bipartite graphs
Proceedings of the 23rd international conference on World wide web
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The main aim of this paper is to design a co-ranking scheme for objects and relations in multi-relational data. It has many important applications in data mining and information retrieval. However, in the literature, there is a lack of a general framework to deal with multi-relational data for co-ranking. The main contribution of this paper is to (i) propose a framework (MultiRank) to determine the importance of both objects and relations simultaneously based on a probability distribution computed from multi-relational data; (ii) show the existence and uniqueness of such probability distribution so that it can be used for co-ranking for objects and relations very effectively; and (iii) develop an efficient iterative algorithm to solve a set of tensor (multivariate polynomial) equations to obtain such probability distribution. Extensive experiments on real-world data suggest that the proposed framework is able to provide a co-ranking scheme for objects and relations successfully. Experimental results have also shown that our algorithm is computationally efficient, and effective for identification of interesting and explainable co-ranking results.