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.
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Proceedings ArticleDOI
Mining Discriminative Patterns to Predict Health Status for Cardiopulmonary Patients
TL;DR: Several universal models to monitor cardiopulmonary conditions are proposed, including DPClass, a novel learning approach designed that yields the highest accuracy while the DPClass model provides better interpretation of the model mechanisms.
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BiTe-GCN: A New GCN Architecture via BidirectionalConvolution of Topology and Features on Text-Rich Networks.
TL;DR: BiTe-GCN is proposed, a novel GCN architecture modeling via bidirectional convolution of topology and features on text-rich networks that outperforms the state-of-the-arts by a breakout improvement and can be applied to several e-commerce search scenes such as JD searching.
Proceedings ArticleDOI
Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks
TL;DR: In this paper, the authors propose a novel framework for minimally supervised text categorization by learning from the text-rich network, which jointly trains two modules with different inductive biases, a text analysis module for text understanding and a network learning module for class discriminative, scalable network learning.
Proceedings ArticleDOI
Exposing Complex Bug-Triggering Conditions in Distributed Systems via Graph Mining
TL;DR: Pop Mine, a tool for diagnosing corner-case bugs by finding the minimal causal directed acyclic graph (DAG) of events, spanning multiple processes, which captures a bug-triggering condition, is introduced.
Posted Content
Task-Guided Pair Embedding in Heterogeneous Network
TL;DR: This paper proposes a novel task-guided pair embedding framework in heterogeneous network, called TaPEm, that directly models the relationship between a pair of nodes that are related to a specific task (e.g., paper-author relationship in author identification).