Publications

2025

  1. wavegc.png
    A General Graph Spectral Wavelet Convolution via Chebyshev Order Decomposition
    Nian Liu, Xiaoxin He, Thomas Laurent, Francesco Di Giovanni, Michael M Bronstein, and Xavier Bresson
    International conference on machine learning, 2025
    arXiv Code

2024

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    Uncovering the Redundancy in Graph Self-supervised Learning Models
    Zhibiao Wang, Xiao Wang, Haoyue Deng, Nian Liu, Shirui Pan, and Chunming Hu
    Advances in Neural Information Processing Systems, 2024
  2. FiDeLiS Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering.png
    FiDeLiS: Faithful Reasoning in Large Language Model for Knowledge Graph Question Answering
    Yuan Sui, Yufei He, Nian Liu, Xiaoxin He, Kun Wang, and Bryan Hooi
    arXiv preprint arXiv:2405.13873, 2024
  3. 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
  4. Provable training for graph contrastive learning
    Yue Yu, Xiao Wang, Mengmei Zhang, Nian Liu, and Chuan Shi
    Advances in Neural Information Processing Systems, 2024
  5. Learning invariant representations of graph neural networks via cluster generalization.png
    Learning invariant representations of graph neural networks via cluster generalization
    Donglin Xia, Xiao Wang, Nian Liu, and Chuan Shi
    Advances in Neural Information Processing Systems, 2024

2023

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    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

2022

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    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
  2. Debiased graph neural networks with agnostic label selection bias.png
    Debiased graph neural networks with agnostic label selection bias
    Shaohua Fan, Xiao Wang, Chuan Shi, Kun Kuang, Nian Liu, and Bai Wang
    IEEE transactions on neural networks and learning systems, 2022
  3. 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

2021

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    Self-supervised heterogeneous graph neural network with co-contrastive learning
    Xiao Wang, Nian Liu, Hui Han, and Chuan Shi
    In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, 2021
    arXiv Code
  2. Lorentzian graph convolutional networks.png
    Lorentzian graph convolutional networks
    Yiding Zhang, Xiao Wang, Chuan Shi, Nian Liu, and Guojie Song
    In Proceedings of the web conference 2021, 2021
  3. Embedding heterogeneous information network in hyperbolic spaces.png
    Embedding heterogeneous information network in hyperbolic spaces
    Yiding Zhang, Xiao Wang, Nian Liu, and Chuan Shi
    ACM Transactions on Knowledge Discovery from Data (TKDD), 2021