PeopleofSuperintelligence

Researcher

Chuan Shi

Also known as Shi Chuan

Chuan Shi works on data mining, machine learning and artificial intelligence, with much of his research focused on heterogeneous graphs. He earned a B.S. at Jilin University in 2001, an M.S. at Wuhan University in 2004 and a Ph.D. from the Institute of Computing Technology in 2007. His work includes HERec for recommendation, FPMP_ESE for entity expansion in knowledge graphs, the Heterogeneous Graph Attention Network and HeCo. He coauthored Advances in Graph Neural Networks and a survey that introduced graph foundation models and grouped methods by their use of graph neural networks and large language models. As of October 2026, he is a professor in the School of Computer Sciences at Beijing University of Posts and Telecommunications.

Works
367
Citations
16,903
h-index
57
Badges

Publication numbers from OpenAlex, as of 22 October 2026.

Timeline

  1. 2001–2007

    Degrees at three institutions

    He earns a B.S. from Jilin University in 2001, an M.S. from Wuhan University in 2004, and a Ph.D. from the Institute of Computing Technology in 2007.

  2. 2017

    HERec for recommendation

    He and three coauthors post the 2017 preprint Heterogeneous Information Network Embedding for Recommendation. HERec uses meta-path random walks to create node embeddings, then combines them with matrix factorization for rating prediction.

  3. 2018

    Entity expansion with meta-paths

    In 2018, he coauthors a method that models a knowledge graph as a heterogeneous network and uses frequent patterns under meta-paths to expand sets from seed entities.

  4. 2019

    Heterogeneous Graph Attention Network

    In 2019, he coauthors the Heterogeneous Graph Attention Network (HAN), which learns from heterogeneous graphs with node-level and semantic-level attention.

  5. 2021

    Self-supervised learning with HeCo

    In 2021, he coauthors HeCo, a self-supervised heterogeneous graph neural network. It uses cross-view contrastive learning across network-schema and meta-path views to learn node representations.

  6. October 2023

    Surveying graph foundation models

    In 2023, he coauthors Graph Foundation Models: Concepts, Opportunities and Challenges, which introduces graph foundation models, classifies approaches by their use of graph neural networks and large language models, and outlines future directions.

  7. November 2023

    A graph neural network book

    In 2023, with Xiao Wang and Cheng Yang, he coauthors Advances in Graph Neural Networks, a book on graph neural network foundations, representation learning and methods for real-world graphs.

  8. As of October 2026

    Professor at BUPT

    As of October 2026, he is a professor in the School of Computer Sciences at Beijing University of Posts and Telecommunications. His research interests include data mining, machine learning and artificial intelligence.

Career

  1. Since 2007Beijing University of Posts and TelecommunicationsProfessor
  2. 2023–2026Peking University
  3. 2020–2024Peng Cheng Laboratory

Education

  1. 2007Chinese Academy of SciencesPh.D.
  2. 2004Wuhan UniversityM.S.
  3. 2001Jilin UniversityB.S.

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

  1. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes Beijing University of Posts and Telecommunications. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.
  2. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes Beijing University of Posts and Telecommunications. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  3. The median h-index is 48 at universities and 14 at frontier AI labsIncludes Beijing University of Posts and Telecommunications. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.
  4. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes Beijing University of Posts and Telecommunications. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Chuan Shi yet. Send us a free-licensed photo.

  1. Advances in Graph Neural Networks by Chuan Shi, (Paperback) | Indigo indigo.ca. Checked 21 October 2026.
  2. Heterogeneous Information Network Embedding for Recommendation arxiv.org. Checked 21 October 2026.
  3. 一种元路径下基于频繁模式的实体集扩展方法 jos.org.cn. Checked 21 October 2026.
  4. Heterogeneous Graph Attention Network arxiv.org. Checked 21 October 2026.
  5. Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning arxiv.org. Checked 21 October 2026.
  6. Graph Foundation Models: Concepts, Opportunities and Challenges arxiv.org. Checked 21 October 2026.
  7. Chuan SHI----The Seventh International Conference on Intelligence Science (ICIS2026) intsci.ac.cn. Checked 21 October 2026.