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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Proceedings ArticleDOI
A Spectral Framework for Detecting Inconsistency across Multi-source Object Relationships
TL;DR: This paper proposes to conduct anomaly detection across multiple sources to identify objects that have inconsistent behavior across these sources by computing the distance between different eigen decomposition results of the same object with respect to different sources as its anomalous score.
Proceedings ArticleDOI
End-to-End Reinforcement Learning for Automatic Taxonomy Induction
TL;DR: The authors proposed an end-to-end reinforcement learning approach to automatic taxonomy induction from a set of terms, where the representations of term pairs are learned using multiple sources of information and used to determine which term to select and where to place it on the taxonomy via a policy network.
Book ChapterDOI
Advanced Pattern Mining
TL;DR: This chapter discusses the advanced methods of frequent pattern mining, which mines more complex forms of frequent patterns and considers user preferences or constraints to speed up the mining process.
Proceedings ArticleDOI
HiGitClass: Keyword-Driven Hierarchical Classification of GitHub Repositories
TL;DR: The HiGitClass framework is proposed, comprising of three modules: heterogeneous information network embedding; keyword enrichment; topic modeling and pseudo document generation, which is superior to existing weakly-supervised and dataless hierarchical classification methods, especially in its ability to integrate both structured and unstructured data for repository classification.
Proceedings ArticleDOI
Meta-Graph Based HIN Spectral Embedding: Methods, Analyses, and Insights
TL;DR: In this paper, the authors proposed an unsupervised approach for HIN embedding by combining multiple meta-graphs to capture the multi-dimensional semantics in HIN through reasoning from mathematical geometry.