RankClus: integrating clustering with ranking for heterogeneous information network analysis
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Ranking-based clustering of heterogeneous information networks with star network schema
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining advisor-advisee relationships from research publication networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph regularized transductive classification on heterogeneous information networks
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Growing parallel paths for entity-page discovery
Proceedings of the 20th international conference companion on World wide web
Ranking-based classification of heterogeneous information networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Co-author Relationship Prediction in Heterogeneous Bibliographic Networks
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
When will it happen?: relationship prediction in heterogeneous information networks
Proceedings of the fifth ACM international conference on Web search and data mining
Mining Heterogeneous Information Networks: Principles and Methodologies
Mining Heterogeneous Information Networks: Principles and Methodologies
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A database contains rich, inter-related, multi-typed data and information, forming one or a set of gigantic, intercon- nected, heterogeneous information networks. Much knowl- edge can be derived from such information networks if we systematically develop an effective and scalable database-oriented information network analysis technology. In this system demo, we take a computer science research publica- tion network as an example, which is an information net- work derived from an integration of DBLP, other web-based information about researchers, and partially available cita- tion data, and construct a Research-Insight system in order to demonstrate the power of database-oriented information network analysis. We show that nontrivial research insight can be obtained from such analysis, including (1) ranking, clustering, classification and similarity search of researchers, terms and venues for research subfields and themes, (2) recommending good researchers and good research papers to read or cite when conducting research on certain topics (3) predicting potential collaborators for certain theme-oriented research, and (4) predicting advisor-advisee rela- tionships and affiliation history based on historical research publications. Although some of these functions have been studied in recent research, effective and scalable realization of such functions in large networks still poses challenging research problems. Moreover, some function are our on- going research tasks. By integrating these functionalities, Research-Insight may not only provide with us insightful rec- ommendations in CS research but also help us gain insight on how to perform effective data mining in large databases.