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
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
Relation strength-aware clustering of heterogeneous information networks with incomplete attributes
Proceedings of the VLDB Endowment
Mining Heterogeneous Information Networks: Principles and Methodologies
Mining Heterogeneous Information Networks: Principles and Methodologies
AMETHYST: a system for mining and exploring topical hierarchies of heterogeneous data
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Real world physical and abstract data objects are interconnected, forming gigantic, interconnected networks. By structuring these data objects into multiple types, such networks become semi-structured heterogeneous information networks. Most real world applications that handle big data, including interconnected social media and social networks, scientific, engineering, or medical information systems, online e-commerce systems, and most database systems, can be structured into heterogeneous information networks. For example, in a medical care network, objects of multiple types, such as patients, doctors, diseases, medication, and links such as visits, diagnosis, and treatments are intertwined together, providing rich information and forming heterogeneous information networks. Effective analysis of large-scale heterogeneous information networks poses an interesting but critical challenge. In this talk, we present a set of data mining scenarios in heterogeneous information networks and show that mining heterogeneous information networks is a new and promising research frontier in data mining research. Departing from many existing network models that view data as homogeneous graphs or networks, the semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and can uncover surprisingly rich knowledge from interconnected data. This heterogeneous network modeling will lead to the discovery of a set of new principles and methodologies for mining interconnected data. The examples to be used in this discussion include (1) meta path-based similarity search, (2) rank-based clustering, (3) rank-based classification, (4) meta path-based link/relationship prediction, (5) relation strength-aware mining, as well as a few other recent developments. We will also point out some promising research directions and provide convincing arguments on that mining heterogeneous information networks is the next frontier in data mining.