# 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

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