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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.

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Proceedings ArticleDOI

Mining Personal Image Collection for Social Group Suggestion

TL;DR: A new approach to cluster popular groups into categories by analyzing the similarity of groups via SimRank is designed, and both visual content and its annotations are integrated to understand the events or topics depicted in the images.
Proceedings ArticleDOI

Investigating Rumor News Using Agreement-Aware Search

TL;DR: Maester is a novel agreement-aware search framework for investigating rumor news that will retrieve related articles to that question, assign and display top articles from agree, disagree, and discuss categories to users, and leverage recurrent neural network to infer the level of agreement.
Journal ArticleDOI

Search for chargino-neutralino production in pp̄ collisions at s=1.96TeV

T. Aaltonen, +621 more
TL;DR: In this paper, the authors present a search for the associated production of charginos and neutralinos in p{bar p} collisions at {radical}s = 1.96 TeV and set limits on the cross section as a function of the chargino mass in three different supersymmetric scenarios.
Proceedings ArticleDOI

Neural Concept Map Generation for Effective Document Classification with Interpretable Structured Summarization

TL;DR: In this paper, a weakly-supervised text-to-graph neural network is proposed to generate concept maps in the middle and is trained towards document-level tasks like document classification.
Proceedings ArticleDOI

AMETHYST: a system for mining and exploring topical hierarchies of heterogeneous data

TL;DR: AMETHYST is a system for exploring and analyzing a topical hierarchy constructed from a heterogeneous information network (HIN), which reflects a domain-specific ontology, interacts with multiple types of linked entities, and can be tailored for both free text and OLAP queries.