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D-Index & Metrics

Computer Science

D-Index
41
Citations
6320
World Ranking
8913
National Ranking
1148

Overview

Senzhang Wang is affiliated with Central South University in China and has contributed extensively to the field of computer science. Their research primarily focuses on artificial intelligence and its applications across various domains including transportation, building and construction, and information systems. The scientist's work spans multiple subfields such as computer vision and pattern recognition, addressing complex challenges through computational methods.

The main topics explored by Senzhang Wang include:

  • Advanced Graph Neural Networks
  • Traffic Prediction and Management Techniques
  • Recommender Systems and Techniques
  • Human Mobility and Location-Based Analysis
  • Topic Modeling
  • Complex Network Analysis Techniques
  • Time Series Analysis and Forecasting

Senzhang Wang has published extensively, with significant contributions in the following venues:

  • arXiv (Cornell University)
  • IEEE Transactions on Knowledge and Data Engineering
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Information Sciences
  • ACM Transactions on Intelligent Systems and Technology

Notable recent publications include:

  • "Deep Learning for Spatio-Temporal Data Mining: A Survey," 2020, IEEE Transactions on Knowledge and Data Engineering
  • "Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting," 2020, Information Sciences
  • "Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning," 2021, Information Sciences
  • "Spatio-Temporal Knowledge Transfer for Urban Crowd Flow Prediction via Deep Attentive Adaptation Networks," 2021, IEEE Transactions on Intelligent Transportation Systems
  • "SeqST-GAN," 2020, ACM Transactions on Spatial Algorithms and Systems

Wang collaborates frequently with other researchers, including:

  • Philip S. Yu
  • Jiannong Cao
  • Chaozhuo Li
  • Hao Peng
  • Hao Miao

The scope of Wang's work integrates machine learning approaches, particularly graph neural networks, to address spatio-temporal data challenges in domains such as urban traffic flow and crowd movement prediction. The research often involves the intersection of data mining, deep learning, and intelligent transportation analytics.

Best Publications

  • Deep Learning for Spatio-Temporal Data Mining: A Survey

    Senzhang Wang;Jiannong Cao;Philip S. Yu

  • Spatial Temporal Incidence Dynamic Graph Neural Networks for Traffic Flow Forecasting

    Hao Peng;Hongfei Wang;Bowen Du;Zakirul Alam Bhuiyan

  • Deep Irregular Convolutional Residual LSTM for Urban Traffic Passenger Flows Prediction

    Bowen Du;Hao Peng;Senzhang Wang;Zakirul Alam Bhuiyan

  • Improving Stock Market Prediction via Heterogeneous Information Fusion

    Xi Zhang;Yunjia Zhang;Senzhang Wang;Yuntao Yao

  • Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning

    Hao Peng;Bowen Du;Mingsheng Liu;Mingzhe Liu

  • Hierarchical Taxonomy-Aware and Attentional Graph Capsule RCNNs for Large-Scale Multi-Label Text Classification

    Hao Peng;Jianxin Li;Senzhang Wang;Lihong Wang

  • Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems

    Wenjing Fu;Zhaohui Peng;Senzhang Wang;Yang Xu

  • MMRate: inferring multi-aspect diffusion networks with multi-pattern cascades

    Senzhang Wang;Xia Hu;Philip S. Yu;Zhoujun Li

  • TrafficGAN: Network-Scale Deep Traffic Prediction With Generative Adversarial Nets

    Yuxuan Zhang;Senzhang Wang;Bing Chen;Jiannong Cao

  • Event detection and popularity prediction in microblogging

    Xiaoming Zhang;Xiaoming Chen;Yan Chen;Senzhang Wang

  • Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks

    Chuan Shi;Xiaotian Han;Li Song;Xiao Wang

  • Applying adaptive over-sampling technique based on data density and cost-sensitive SVM to imbalanced learning

    Senzhang Wang;Zhoujun Li;Wenhan Chao;Qinghua Cao

  • Influence Maximization Across Partially Aligned Heterogenous Social Networks

    Qianyi Zhan;Jiawei Zhang;Senzhang Wang;Philip S. Yu;Philip S. Yu

  • Citywide traffic congestion estimation with social media

    Senzhang Wang;Lifang He;Leon Stenneth;Philip S. Yu

  • Computing Urban Traffic Congestions by Incorporating Sparse GPS Probe Data and Social Media Data

    Senzhang Wang;Xiaoming Zhang;Jianping Cao;Lifang He

  • Adversarial Learning for Weakly-Supervised Social Network Alignment.

    Chaozhuo Li;Senzhang Wang;Yukun Wang;Philip S. Yu

  • PPNE: Property Preserving Network Embedding

    Chaozhuo Li;Senzhang Wang;Dejian Yang;Zhoujun Li

  • Aspect-Level Deep Collaborative Filtering via Heterogeneous Information Networks

    Xiaotian Han;Chuan Shi;Senzhang Wang;Philip S. Yu;Philip S. Yu

  • Multi-task Adversarial Spatial-Temporal Networks for Crowd Flow Prediction

    Senzhang Wang;Hao Miao;Hao Chen;Zhiqiu Huang

  • Spatio-Temporal Knowledge Transfer for Urban Crowd Flow Prediction via Deep Attentive Adaptation Networks

    Senzhang Wang;Hao Miao;Jiyue Li;Jiannong Cao

  • Burst time prediction in cascades

    Senzhang Wang;Zhao Yan;Xia Hu;Philip S. Yu

Frequent Co-Authors

Philip S. Yu
Philip S. Yu University of Illinois at Chicago
Zhoujun Li
Zhoujun Li Beihang University
Jiannong Cao
Jiannong Cao Hong Kong Polytechnic University
Lifang He
Lifang He Lehigh University
Jianxin Li
Jianxin Li Tianjin Polytechnic University
Fei-Yue Wang
Fei-Yue Wang Chinese Academy of Sciences
Xia Hu
Xia Hu Rice University
Chuan Shi
Chuan Shi Beijing University of Posts and Telecommunications
Wenbing Huang
Wenbing Huang Renmin University of China
Zakirul Alam Bhuiyan
Zakirul Alam Bhuiyan Fordham University

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