Shichang Zhang

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About Me

I am currently a forth-year Ph.D. student in Computer Science at University of California, Los Angeles (UCLA) Data Mining Lab working with Professor Yizhou Sun. Before joining UCLA, I received my B.A. and M.S. both in Statistics from Berkeley and Stanford respectively. [CV]

I am organizing the UCLA Data Mining Reading Group. We cover interesting graph machine learning papers every week. Please check the past slides and recordings here. The reading group also dig deep into one interesting AI topic each quarter. The topic for this quarter is Neural Radiance Fields (NeRF). In the past, we have covered diffusion models, large language models, differential geometry, graphical models, and spectral graph theory. Please find past slides and recordings here. My personal presentations at the reading group can be found under Talks.


3551 (ScAI Lab), Bolter Hall, UCLA, CA, 90095
E-mail: shichang AT cs DOT ucla DOT edu
[Google Scholar] [GitHub] [Twitter] [LinkedIn]

What's New

  • [April 2023] One paper on GNN distillation for link prediction is accepted by ICML 2023 [PDF]

  • [Feb 2023] Give a talk on the GStarX paper at AI TIME NeurIPS Talk Series [slides][video (in Chinese, starting from 00:19:05)]

  • [Jan 2023] One paper on GNN explanation for link prediction is accepted by WWW 2023 [PDF]

  • [Sept 2022] One paper on GNN explanation is accepted by NeurIPS 2022 [Paper]

  • [July 2022] Selected as the top 10% of reviewers for ICML 2022

Find out older news

Research Interests

My research interests include

  • Graph Neural Network

  • Explainable AI

  • Trustworthy AI

  • Model Efficiency

  • Causality

  • Self-supervised Learning

Publications and Pre-prints

  1. PaGE-Link: Graph Neural Network Explanation for Heterogeneous Link Prediction (WWW 2023)
    Shichang Zhang, Jiani Zhang, Xiang Song, Soji Adeshina, Da Zheng, Christos Faloutsos, Yizhou Sun. [PDF][code]

  2. Linkless Link Prediction via Relational Distillation (ICML 2023)
    Zhichun Guo, William Shiao, Shichang Zhang, Yozen Liu, Nitesh Chawla, Neil Shah, Tong Zhao [PDF]

  3. GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games (NeurIPS 2022)
    Shichang Zhang, Neil Shah, Yozen Liu, Yizhou Sun [PDF] [code]

  4. Graph-less Neural Networks, Teach Old MLPs New Tricks via Distillation (ICLR 2022)
    Shichang Zhang, Yozen Liu, Yizhou Sun, Neil Shah [PDF] [code]

  5. Graph Condensation for Graph Neural Networks (ICLR 2022)
    Wei Jin, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang, Neil Shah [PDF] [code]

  6. Motif-driven Contrastive Learning of Graph Representations (SSL@WWW2021)
    Shichang Zhang, Ziniu Hu, Arjun Subramonian, Yizhou Sun [PDF]

Full list of publications