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
Fast computation of SimRank for static and dynamic information networks
TL;DR: A family of novel approximate SimRank computation algorithms for static and dynamic information networks are developed and their corresponding theoretical justification and analysis are given.
Proceedings ArticleDOI
PET: a statistical model for popular events tracking in social communities
TL;DR: This paper formally defines the problem of popular event tracking in online communities (PET) and proposes a novel statistical method that models the the popularity of events over time, taking into consideration the burstiness of user interest, information diffusion on the network structure, and the evolution of textual topics.
Proceedings ArticleDOI
Recommendation in heterogeneous information networks with implicit user feedback
Xiao Yu,Xiang Ren,Yizhou Sun,Bradley Sturt,Urvashi Khandelwal,Quanquan Gu,Brandon Norick,Jiawei Han +7 more
TL;DR: This paper proposes to combine various relationship information from the network with user feedback to provide high quality recommendation results and uses meta-path-based latent features to represent the connectivity between users and items along different paths in the related information network.
Journal ArticleDOI
Stream Cube: An Architecture for Multi-Dimensional Analysis of Data Streams
TL;DR: This paper proposes an architecture, called stream_cube, to facilitate on-line, multi-dimensional,multi-level analysis of stream data, and proposes an efficient stream data cubing algorithm which computes only the layers (cuboids) along a popular path and leaves the other cuboids for query-driven, on- line computation.
Proceedings ArticleDOI
On Discovery of Traveling Companions from Streaming Trajectories
Lu-An Tang,Lu-An Tang,Yu Zheng,Jing Yuan,Jing Yuan,Jiawei Han,Alice Leung,Chih-Chieh Hung,Wen-Chih Peng +8 more
TL;DR: A new data structure termed traveling buddy is designed to facilitate scalable and flexible companion discovery on trajectory stream that is an order of magnitude faster than existing methods and outperforms other competitors with higher precision and recall in companion discovery.