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
Curriculum Learning for Heterogeneous Star Network Embedding via Deep Reinforcement Learning
Meng Qu,Jian Tang,Jiawei Han +2 more
TL;DR: This paper proposes an approach based on deep reinforcement learning for node representation learning in heterogeneous star networks, which leverages LSTM models to encode states and further estimate the expected cumulative reward of each state-action pair.
Journal ArticleDOI
Recurring and Novel Class Detection Using Class-Based Ensemble for Evolving Data Stream
Tahseen M. Al-Khateeb,Mohammad M. Masud,Khaled Al-Naami,Sadi Evren Seker,Ahmad Mustafa,Latifur Khan,Zouheir Trabelsi,Charu C. Aggarwal,Jiawei Han +8 more
TL;DR: This paper defines a novel ensemble technique “class-based” ensemble which replaces the traditional “chunk- based” approach in order to detect the recurring classes in data streams and proves the superiority of both “ class-based" ensemble method over state-of-art techniques via empirical approach on a number of benchmark data sets.
Proceedings ArticleDOI
TaxoClass: Hierarchical Multi-Label Text Classification Using Only Class Names
TL;DR: This paper proposes a novel HMTC framework, named TaxoClass, which calculates document-class similarities using a textual entailment model, identifies a document’s core classes and utilizes confident core classes to train a taxonomy-enhanced classifier, and generalizes the classifier via multi-label self-training.
Proceedings ArticleDOI
TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network
TL;DR: Wang et al. as discussed by the authors propose a self-supervised framework, named TaxoExpan, which automatically generates a set of query concept, anchor concept pairs from the existing taxonomy as training data.
Proceedings ArticleDOI
Modeling Probabilistic Measurement Correlations for Problem Determination in Large-Scale Distributed Systems
TL;DR: The proposed transition probability model based on markov properties to characterize pair-wise measurement correlations can discover both the spatial and temporal correlations in the distributed system and thus can successfully represent the system normal profiles.