Paper Notes

I just list some paper notes here to push myself. If you find that we share similar reseach interests, feel free to drop me an email for discussions and collaborations!

May 2022

  1. [arXiv 2022] GPN: A Joint Structural Learning Framework for Graph Neural Networks [Paper | Note]
  2. [NeurIPS 2021] Building Powerful and Equivariant Graph Neural Networks with Structural Message-Passing [Paper | Note]
  3. [NeurIPS 2021] Not All Low-Pass Filters are Robust in Graph Convolutional Networks [Paper | Note]
  4. [ICLR-GTRL 2022] Diversified Multiscale Graph Learning with Graph Self-Correction [Paper | Note]
  5. [NeurIPS 2020] Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings [Paper | Note]
  6. [WWW 2022] Towards Unsupervised Deep Graph Structure Learning [Paper | Note]
  7. [IJCAI 2022] Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal Transport [Paper | Note]
  8. [arXiv 2022] Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs [Paper | Note]
  9. [IJCAI 2021] On Self-Distilling Graph Neural Network [Paper | Note]
  10. [arXiv 2022] FMP: Toward Fair Graph Message Passing against Topology Bias [Paper | Note]
  11. [NeurIPS 2021] SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks [Paper | Note ]
  12. [ICML 2022] p-Laplacian Based Graph Neural Networks [Paper | Note]
  13. [NeurIPS 2020] Self-Supervised Graph Transformer on Large-Scale Molecular Data [Paper | Note]
Last Updated: 5/24/2022, 3:55:12 AM