Topic-level social network search
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Cross-domain collaboration recommendation
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
PatentMiner: topic-driven patent analysis and mining
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Foundations and Trends in Information Retrieval
Patent partner recommendation in enterprise social networks
Proceedings of the sixth ACM international conference on Web search and data mining
"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
Mining frequent neighborhood patterns in a large labeled graph
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
A Local Method for ObjectRank Estimation
Proceedings of International Conference on Information Integration and Web-based Applications & Services
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In this paper, we present a topic level expertise search framework for heterogeneous networks. Different from the traditional Web search engines that perform retrieval and ranking at document level (or at object level), we investigate the problem of expertise search at topic level over heterogeneous networks. In particular, we study this problem in an academic search and mining system, which extracts and integrates the academic data from the distributed Web. We present a unified topic model to simultaneously model topical aspects of different objects in the academic network. Based on the learned topic models, we investigate the expertise search problem from three dimensions: ranking, citation tracing analysis, and topical graph search. Specifically, we propose a topic level random walk method for ranking the different objects. In citation tracing analysis, we aim to uncover how a piece of work influences its follow-up work. Finally, we have developed a topical graph search function, based on the topic modeling and citation tracing analysis. Experimental results show that various expertise search and mining tasks can indeed benefit from the proposed topic level analysis approach.