NIAN LIU / 刘 念

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I am a second year Ph.D. student in the School of Computing at National University of Singapore (NUS) with President’s Graduate Fellowship, advised by Prof. Xavier Bresson. Before joining NUS, I received my M.S. and B.E. at Beijing University of Posts and Telecommunications, supervised by Prof. Chuan Shi and Prof. Xiao Wang.

My research interests include Graph Neural Network, Graph Transformer, Graph Signal Processing and Graph Self-supervised Learning. Now, I am engaging in the graph diffusion generation technique and its applications on AI4Material.

"Success is not final, failure is not fatal: It is the courage to continue that counts."
-- Winston Churchill

News

Mar 15, 2024 One paper is accepted by DASFAA 2024.
Oct 01, 2023 One paper is accepted by IEEE TKDE.
Sep 22, 2023 Two papers are accepted by NeurIPS 2023.
Aug 07, 2023 Become a NUS PhD candidate, supervised by Prof. Xavier Bresson :sparkles: :sparkles:

Selected Publications

  1. wavegc.png
    Advancing Graph Convolutional Networks via General Spectral Wavelets
    Nian Liu, Xiaoxin He, Thomas Laurent, Francesco Di Giovanni, Michael M Bronstein, and Xavier Bresson
    arXiv preprint arXiv:2405.13806, 2024
    arXiv Code
  2. lsir.png
    Learning Social Graph for Inactive User Recommendation
    Nian Liu, Shen Fan, Ting Bai, Peng Wang, Mingwei Sun, Yanhu Mo, Xiaoxiao Xu, Hong Liu, and Chuan Shi
    In International Conference on Database Systems for Advanced Applications, 2024
    arXiv Code
  3. heco++.png
    Hierarchical contrastive learning enhanced heterogeneous graph neural network
    Nian Liu, Xiao Wang, Hui Han, and Chuan Shi
    IEEE Transactions on Knowledge and Data Engineering, 2023
    arXiv
  4. spco.png
    Revisiting graph contrastive learning from the perspective of graph spectrum
    Nian Liu, Xiao Wang, Deyu Bo, Chuan Shi, and Jian Pei
    Advances in Neural Information Processing Systems, 2022
    arXiv Code
  5. cogsl.png
    Compact graph structure learning via mutual information compression
    Nian Liu, Xiao Wang, Lingfei Wu, Yu Chen, Xiaojie Guo, and Chuan Shi
    In Proceedings of the ACM web conference 2022, 2022
    arXiv Code

Academic Service

  • Conference Reviewer -- ICML 24, NeurIPS 24, ICLR 25, KDD 25, AAAI 25, LOG 24
  • Journal Reviewer -- IEEE TAI

Teaching Assistant

  • 2024/2025 semester 1, CS5284 Graph Machnie Learning