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@@ -99,6 +99,9 @@ The folder contains example implementations of selected research papers related
| [Combining Label Propagation and Simple Models Out-performs Graph Neural Networks](#correct_and_smooth) | :heavy_check_mark: | | | | :heavy_check_mark: |
| [Learning from Labeled and Unlabeled Data with Label Propagation](#label_propagation) | :heavy_check_mark: | | | | |
| [Heterogeneous Graph Neural Network](#hetgnn) | :heavy_check_mark: | :heavy_check_mark: | | :heavy_check_mark: | |
| [Graph Transformer Networks](#gtn) | :heavy_check_mark: | | | | |
| [Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding](#magnn) | :heavy_check_mark: | | | | |
| [Network Schema Preserving Heterogeneous Information Network Embedding](#nshe) | :heavy_check_mark: | | | | |
## 2021
@@ -156,6 +159,9 @@ The folder contains example implementations of selected research papers related
- <a name="deepergcn"></a> Li et al. DeeperGCN: All You Need to Train Deeper GCNs. [Paper link](https://arxiv.org/abs/2006.07739).
- Example code: [PyTorch](../examples/pytorch/deepergcn)
- Tags: over-smoothing, deeper gnn, OGB
- <a name="magnn"></a> Fu X, et al. MAGNN: metapath aggregated graph neural network for heterogeneous graph embedding. [Paper link](https://dl.acm.org/doi/abs/10.1145/3366423.3380297).
- Example code: [PyTorch](https://github.com/BUPT-GAMMA/OpenHGNN/tree/main/openhgnn/output/MAGNN)
- Tags: Heterogeneous graphs, Graph neural networks, Graph embedding
## 2019
@@ -187,7 +193,7 @@ The folder contains example implementations of selected research papers related
- Example code: [PyTorch](../examples/pytorch/graphwriter)
- Tags: knowledge graph, text generation
- <a name="han"></a> Wang et al. Heterogeneous Graph Attention Network. [Paper link](https://arxiv.org/abs/1903.07293).
- Example code: [PyTorch](../examples/pytorch/han)
- Example code: [PyTorch](../examples/pytorch/han), [OpenHGNN](https://github.com/BUPT-GAMMA/OpenHGNN/tree/main/openhgnn/output/HAN)
- Tags: heterogeneous graphs, node classification
- <a name="lgnn"></a> Chen et al. Supervised Community Detection with Line Graph Neural Networks. [Paper link](https://arxiv.org/abs/1705.08415).
- Example code: [PyTorch](../examples/pytorch/line_graph)
@@ -235,8 +241,14 @@ The folder contains example implementations of selected research papers related
- Example code: [PyTorch](../examples/pytorch/gnn_explainer)
- Tags: Graph Neural Network, Explainability
- <a name='hetgnn'></a> Zhang C, Song D, et al. Heterogeneous graph neural network. [Paper link](https://dl.acm.org/doi/abs/10.1145/3292500.3330961).
- Example code: [PyTorch](https://github.com/BUPT-GAMMA/OpenHGNN/tree/main/openhgnn/output/HetGNN)
- Tags: Heterogeneous graphs, Graph neural networks, Graph embedding
- Example code: [OpenHGNN](https://github.com/BUPT-GAMMA/OpenHGNN/tree/main/openhgnn/output/HetGNN)
- Tags: Heterogeneous graphs, Graph neural networks, Graph embedding
- <a name='gtn'></a> Yun S, Jeong M, et al. Graph transformer networks. [Paper link](https://arxiv.org/abs/1911.06455).
- Example code: [OpenHGNN](https://github.com/BUPT-GAMMA/OpenHGNN/tree/main/openhgnn/output/MAGNN)
- Tags: Heterogeneous graphs, Graph neural networks, Graph structure
- <a name='nshe'></a> Zhao J, et al. Network Schema Preserving Heterogeneous Information Network Embedding. [Paper link](https://www.ijcai.org/Proceedings/2020/0190.pdf).
- Example code: [OpenHGNN](https://github.com/BUPT-GAMMA/OpenHGNN/tree/main/openhgnn/output/NSHE)
- Tags: Heterogeneous graphs, Graph neural networks, Graph structure
## 2018