World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
6007
World Ranking
10740
National Ranking
4484

Overview

Xiangnan Kong is affiliated with Worcester Polytechnic Institute in the United States. Their research primarily spans the field of Computer Science, with a focus on several subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging, as well as Statistical and Nonlinear Physics.

The scientist's publications cover a range of topics related to machine learning and data analysis. Key areas of work include:

  • Machine Learning in Healthcare
  • Functional Brain Connectivity Studies
  • Advanced Graph Neural Networks
  • Topic Modeling
  • Explainable Artificial Intelligence (XAI)
  • Medical Image Segmentation Techniques
  • Domain Adaptation and Few-Shot Learning

Notable recent papers authored or co-authored by Xiangnan Kong include:

  • Role-Based Graph Embeddings, 2020, IEEE Transactions on Knowledge and Data Engineering
  • Sound absorption performance of micro-perforated plate sandwich structure based on triply periodic minimal surface, 2023, Journal of Materials Research and Technology
  • Sound absorption performance of a micro-perforated plate sandwich structure based on selective laser melting, 2024, Virtual and Physical Prototyping
  • Semi-supervised Co-Clustering on Attributed Heterogeneous Information Networks, 2020, Information Processing & Management
  • Off-Deployment Traffic Estimation - A Traffic Generative Adversarial Networks Approach, 2020, IEEE Transactions on Big Data

The scientist has frequently published in venues such as:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • 2022 IEEE International Conference on Data Mining (ICDM)
  • IEEE Transactions on Knowledge and Data Engineering
  • Journal of Materials Research and Technology

Frequent collaborators include:

  • Thomas Hartvigsen
  • Elke A. Rundensteiner
  • Jidapa Thadajarassiri
  • Yao Su
  • John Boaz Lee

Best Publications

  • 2014 IEEE International Conference on Data Mining

    Aleksandr Aravkin;Aurelie Lozano;Ronny Luss;Prabhajan Kambadur

  • Inferring anchor links across multiple heterogeneous social networks

    Xiangnan Kong;Jiawei Zhang;Philip S. Yu

  • HeteSim: A General Framework for Relevance Measure in Heterogeneous Networks

    Chuan Shi;Xiangnan Kong;Yue Huang;Philip S. Yu

  • Graph Classification using Structural Attention

    John Boaz Lee;Ryan Rossi;Xiangnan Kong

  • Inferring the impacts of social media on crowdfunding

    Chun-Ta Lu;Sihong Xie;Xiangnan Kong;Philip S. Yu

  • Transductive Multilabel Learning via Label Set Propagation

    Xiangnan Kong;M. K. Ng;Zhi-Hua Zhou

  • Meta path-based collective classification in heterogeneous information networks

    Xiangnan Kong;Philip S. Yu;Ying Ding;David J. Wild

  • Semi-supervised feature selection for graph classification

    Xiangnan Kong;Philip S. Yu

  • Transferring heterogeneous links across location-based social networks

    Jiawei Zhang;Xiangnan Kong;Philip S. Yu

  • Relevance search in heterogeneous networks

    Chuan Shi;Xiangnan Kong;Philip S. Yu;Sihong Xie

  • Predicting Social Links for New Users across Aligned Heterogeneous Social Networks

    Jiawei Zhang;Xiangnan Kong;Philip S. Yu

  • Community detection in incomplete information networks

    Wangqun Lin;Xiangnan Kong;Philip S. Yu;Quanyuan Wu

  • Deep Collective Classification in Heterogeneous Information Networks

    Yizhou Zhang;Yun Xiong;Xiangnan Kong;Shanshan Li

  • Active Learning: A Survey

    Charu C. Aggarwal;Xiangnan Kong;Quanquan Gu;Jiawei Han

  • Multi-label classification by mining label and instance correlations from heterogeneous information networks

    Xiangnan Kong;Bokai Cao;Philip S. Yu

  • Learning Role-based Graph Embeddings.

    Nesreen K. Ahmed;Ryan A. Rossi;John Boaz Lee;Xiangnan Kong

  • Graph Convolutional Networks with Motif-based Attention

    John Boaz Lee;Ryan A. Rossi;Xiangnan Kong;Sungchul Kim

  • Role-based Graph Embeddings

    Nesreen Ahmed;Ryan Anthony Rossi;John Lee;Theodore Willke

  • Collective Prediction of Multiple Types of Links in Heterogeneous Information Networks

    Bokai Cao;Xiangnan Kong;Philip S. Yu

  • DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages.

    Lifang He;Xiangnan Kong;Philip S. Yu;Ann B. Ragin

  • Meta Path-Based Collective Classification in Heterogeneous Information Networks

    Xiangnan Kong;Bokai Cao;Philip S. Yu;Ying Ding

Frequent Co-Authors

Philip S. Yu
Philip S. Yu University of Illinois at Chicago
Elke A. Rundensteiner
Elke A. Rundensteiner Worcester Polytechnic Institute
Chuan Shi
Chuan Shi Beijing University of Posts and Telecommunications
Xiao Qin
Xiao Qin Auburn University
Lifang He
Lifang He Lehigh University
Nesreen K. Ahmed
Nesreen K. Ahmed Intel (United States)
Ying Ding
Ying Ding The University of Texas at Austin
Charu C. Aggarwal
Charu C. Aggarwal IBM (United States)
Bin Wu
Bin Wu Beijing University of Posts and Telecommunications
Li-Jia Li
Li-Jia Li Stanford University

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