J
Jiawei Han
Researcher at University of Illinois at Urbana–Champaign
Publications - 1302
Citations - 155054
Jiawei Han is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Cluster analysis & Knowledge extraction. The author has an hindex of 168, co-authored 1233 publications receiving 143427 citations. Previous affiliations of Jiawei Han include Georgia Institute of Technology & United States Army Research Laboratory.
Papers
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Journal ArticleDOI
Latent Community Topic Analysis: Integration of Community Discovery with Topic Modeling
TL;DR: The model separates the concepts of community and topic, so one community can correspond to multiple topics and multiple communities can share the same topic, and confirms the hypothesis that topics could help understand community structure, while community structure could help model topics.
Book ChapterDOI
CrossMine: efficient classification across multiple database relations
TL;DR: CrossMine as discussed by the authors employs tuple ID propagation, a novel method for virtually joining relations, which enables flexible and efficient search among multiple relations, and uses aggregated information to provide essential statistics for classification.
Proceedings Article
Towards graph containment search and indexing
TL;DR: Experimental results on real test data show that cIndex achieves near-optimal pruning power on various containment search workloads, and confirms its obvious advantage over indices built for traditional graph search in this new scenario.
Proceedings ArticleDOI
Community evolution detection in dynamic heterogeneous information networks
TL;DR: A Dirichlet Process Mixture Model-based generative model is proposed to model the community generations and the evolution structure can be read from the model, which can help users better understand the birth, split and death of communities.
Journal ArticleDOI
A network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression.
TL;DR: A new clustering algorithm for cancer subtype identification, called “network-assisted co-clustering for the identification of cancer subtypes” (NCIS), which combines gene network information to simultaneously group samples and genes into biologically meaningful clusters.