This repository mainly lists some the latest research on graph neural network theory.
| Survey | |
| Spectral Domains | |
| Spatial Domains | |
| Expressive Power | |
| Dynamic Graph | |
| Application |
| Name | Paper | Venue | Year | Code | Hint |
|---|---|---|---|---|---|
| Survey | A Comprehensive Survey on Graph Neural Networks | arxiv | 2019 | ||
| Survey | Graph neural networks: A review of methods and applications | ScienceDirect | 2020 | ||
| Survey | Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks | arxiv | 2020 |
| Name | Paper | Venue | Year | Code | Hint |
|---|---|---|---|---|---|
| Spectral Networks and Deep Locally Connected Networks on Graphs | arxiv | 2013 | |||
| Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering | NIPS | 2016 | |||
| GCN | SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS | ICLR | 2017 | Pytorch | |
| BernNet | BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation | arxiv | 2019 | Pytorch | |
| GPR-GNN | ADAPTIVE UNIVERSAL GENERALIZED PAGERANK GRAPH NEURAL NETWORK | ICLR | 2021 | Pytorch | |
| EvenNet | EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks | NIPS | 2022 | Code | |
| How Powerful are Spectral Graph Neural Networks | ICLR | 2022 | |||
| FavardGNN | Graph Neural Networks with Learnable and Optimal Polynomial Bases | arxiv | 2023 | Pytorch | |
| LON-GNN | LON-GNN: Spectral GNNs with Learnable Orthonormal Basis | arxiv | 2023 | Pytorch |
| Name | Paper | Venue | Year | Code | Hint |
|---|---|---|---|---|---|
| MPNNs | Neural Message Passing for Quantum Chemistry | arxiv | 2017 | ||
| SGC | Simplifying Graph Convolutional Networks | ICML | 2019 | ||
| Can Graph Neural Networks Count Substructures? | NIPS | 2020 | Code | ||
| GNNML3 | Breaking the Limits of Message Passing Graph Neural Networks | ICML | 2021 | ||
| MESSAGE PASSING ALL THE WAY UP | arxiv | 2022 | |||
| Shortest Path Networks for Graph Property Prediction | arxiv | 2022 | |||
| Towards Training GNNs using Explanation Directed Message Passing | ICLR | 2022 | |||
| ANISOTROPIC MESSAGE PASSING: GRAPH NEURAL NETWORKS WITH DIRECTIONAL AND LONG-RANGE INTERACTIONS | ICLR | 2023 | |||
| FUNDAMENTAL LIMITS IN FORMAL VERIFICATION OF MESSAGE-PASSING NEURAL NETWORKS | ICLR | 2023 |
| Name | Paper | Venue | Year | Code | Hint |
|---|---|---|---|---|---|
| Survey | Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey | IEEE Access | 2021 | ||
| Survey | Encoder-Decoder Architecture for Supervised Dynamic Graph Learning: A Survey | arxiv | 2022 | ||
| Information Theoretically Optimal Sample Complexity of Learning Dynamical Directed Acyclic Graphs | arxiv | 2023 | |||
| Reversible and irreversible bracket-based dynamics for deep graph neural networks | arxiv | 2023 | |||
| PIGNN | Continual Learning on Dynamic Graphs via Parameter Isolation | arxiv | 2023 | ||
| Analysis of different temporal graph neural network configurations on dynamic graphs | arxiv | 2023 | |||
| arxiv | 2023 |
| Name | Paper | Venue | Year | Code | Hint |
|---|---|---|---|---|---|
| TGC | How Expressive are Spectral-Temporal Graph Neural Networks for Time Series Forecasting? | arxiv | 2023 | ||
| RDGT | Recurrent Transformer for Dynamic Graph Representation Learning with Edge Temporal States | arxiv | 2023 | ||
| Auto-HeG | Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs | arxiv | 2023 | ||
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