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
Automatic Entity Recognition and Typing in Massive Text Corpora
TL;DR: This tutorial introduces data-driven methods to recognize typed entities of interest in different kinds of text corpora (especially in massive, domain-specificText corpora) and demonstrates on real datasets how these typed entities aid in knowledge discovery and management.
Proceedings ArticleDOI
Unsupervised Differentiable Multi-aspect Network Embedding
TL;DR: A novel end-to-end framework for multi-aspect network embedding, called asp2vec, in which the aspects of each node are dynamically assigned based on its local context, and the aspect regularization framework is introduced to capture the interactions among the multiple aspects in terms of relatedness and diversity.
Proceedings ArticleDOI
ARCube: supporting ranking aggregate queries in partially materialized data cubes
Tianyi Wu,Dong Xin,Jiawei Han +2 more
TL;DR: This work proposes a query execution model to answer different types of ranking aggregate queries based on a unified, partial cube structure, ARCube, and identifies a bounding principle for effective pruning, which addresses the problem of efficient online candidate aggregation and verification.
Journal ArticleDOI
Exploring and inferring user-user pseudo-friendship for sentiment analysis with heterogeneous networks
TL;DR: A novel information network‐based framework which can infer hidden similarity and dissimilarity between users by exploring similar and opposite opinions is proposed, so as to improve post‐level and user‐level sentiment classification at the same time.