Multi-document summarization using cluster-based link analysis
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Learning to recognize reliable users and content in social media with coupled mutual reinforcement
Proceedings of the 18th international conference on World wide web
Telling experts from spammers: expertise ranking in folksonomies
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Enhancing multilingual latent semantic analysis with term alignment information
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Measuring scholarly impact in heterogeneous networks
Proceedings of the 73rd ASIS&T Annual Meeting on Navigating Streams in an Information Ecosystem - Volume 47
Topic-driven multi-type citation network analysis
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Ranking authors in digital libraries
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Competition-based user expertise score estimation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Award prediction with temporal citation network analysis
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
MultiRank: co-ranking for objects and relations in multi-relational data
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the “Voice of the Customer” for Business Prioritization
ACM Transactions on Intelligent Systems and Technology (TIST)
Relation extraction for monitoring economic networks
NLDB'09 Proceedings of the 14th international conference on Applications of Natural Language to Information Systems
Significant node identification in social networks
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Bimodal invitation-navigation fair bets model for authority identification in a social network
Proceedings of the 21st international conference on World Wide Web
BibRank: a language-based model for co-ranking entities in bibliographic networks
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Modeling and exploiting heterogeneous bibliographic networks for expertise ranking
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Journal of the American Society for Information Science and Technology
Tweet recommendation with graph co-ranking
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Towards an effective and unbiased ranking of scientific literature through mutual reinforcement
Proceedings of the 21st ACM international conference on Information and knowledge management
Enhanced Information Retrieval by Exploiting Recommender Techniques in Cluster-Based Link Analysis
Proceedings of the 2013 Conference on the Theory of Information Retrieval
Efficient image and tag co-ranking: a bregman divergence optimization method
Proceedings of the 21st ACM international conference on Multimedia
Discovering influential authors in heterogeneous academic networks by a co-ranking method
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
A local social network approach for research management
Decision Support Systems
Topic segmentation and labeling in asynchronous conversations
Journal of Artificial Intelligence Research
Reduce and aggregate: similarity ranking in multi-categorical bipartite graphs
Proceedings of the 23rd international conference on World wide web
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Recent graph-theoretic approaches have demonstrated remarkable successes for ranking networked entities, but most of their applications are limited to homogeneous networks such as the network of citations between publications. This paper proposes a novel method for co-ranking authors and their publications using several networks: the social network connecting the authors, the citation network connecting the publications, as well as the authorship network that ties the previous two together. The new co-ranking framework is based on coupling two random walks, that separately rank authors and documents following the PageRank paradigm. As a result, improved rankings of documents and their authors depend on each other in a mutually reinforcing way, thus taking advantage of the additional information implicit in the heterogeneous network of authors and documents.