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Xiangjie Kong
Researcher at Zhejiang University of Technology
Publications - 161
Citations - 6003
Xiangjie Kong is an academic researcher from Zhejiang University of Technology. The author has contributed to research in topics: Computer science & The Internet. The author has an hindex of 37, co-authored 152 publications receiving 3929 citations. Previous affiliations of Xiangjie Kong include Dalian University of Technology & Zhejiang University.
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VISOS: A Visual Interactive System for Spatial-Temporal Exploring Station Importance Based on Subway Data
TL;DR: A visual interactive subway system that incorporates subway data visualization module, spatial–temporal exploration module, and station clustering module to explore the subway data interactively, analyze human mobility pattern responsively, and identify functional characteristics of subway stations precisely is proposed.
Posted Content
Quantifying the Impact of Scholarly Papers Based on Higher-Order Weighted Citations
TL;DR: Capturing the citation dynamics with higher-order dependencies reveals the actual impact of papers, including necessary self-citations that are sometimes excluded in prior studies.
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Multi-Feature Representation Based COVID-19 Risk Stage Evaluation With Transfer Learning
TL;DR: A multi-feature representation based COVID-19 risk stage evaluation model with transfer learning (COV2RS) is proposed to solve the challenges of the complexity of the epidemic spread and the lack of data in countries where CO VID-19 has recently emerged.
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Turing number: how far are you to A. M. Turing award?
TL;DR: The Turing Number (TN) index is proposed to measure how far a specific scholar is to any Turing Award Laureate and it is demonstrated that TN has the potential of reflecting a scholar's academic influence and reputation.
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Recurrent-DC: A deep representation clustering model for university profiling based on academic graph
TL;DR: A novel University Profiling Framework (UPF) is proposed from the production and complexity point of view which is different from other straightforward solutions and shows that good representations for university clustering task-specific problem can be learned over multiple timesteps.