initial commit

.gitignore

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+#common
+**/*.DS_Store
+**/*.ipynb_checkpoints/
+**/__pycache__
+out/
+
+gmail.py
+
+#SBM datasets
+data/SBMs/*.pkl
+
+#Superpixels datasets
+data/superpixels/*.pkl
+data/superpixels/*.zip
+PATH/
+
+#ZINC dataset
+data/molecules/*.pkl
+data/molecules/*.pickle
+data/molecules/*.zip
+
+#TSP
+data/TSP/*.txt
+data/TSP/*.pkl
+data/TSP/*.zip
+data/TSP/pyconcorde/
+
+

README.md

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+# Benchmarking Graph Neural Networks
+
+<img src="./docs/gnns.jpg" align="right" width="350"/>
+
+<br>
+
+## 1. Benchmark installation
+
+[Follow these instructions](./docs/01_benchmark_installation.md) to install the benchmark and setup the environment.
+
+
+<br>
+
+## 2. Download datasets
+
+[Proceed as follows](./docs/02_download_datasets.md) to download the benchmark datasets.
+
+
+<br>
+
+## 3. Reproducibility 
+
+[Use this page](./docs/03_run_codes.md) to run the codes and reproduce the published results.
+
+
+<br>
+
+## 4. Adding a new dataset 
+
+[Instructions](./docs/04_add_dataset.md) to add a dataset to the benchmark.
+
+
+<br>
+
+## 5. Adding a new graph neural network
+
+[Step-by-step directions](./docs/05_add_gnn.md) to add a GNN to the benchmark.
+
+
+
+
+
+<br>
+
+## 6. Reference 
+
+```
+@article{dwivedi2020benchmarkgnns,
+  title={Benchmarking Graph Neural Networks},
+  author={Dwivedi, Vijay Prakash and Joshi, Chaitanya K and Laurent, Thomas and Bengio, Yoshua and Bresson, Xavier},
+  journal={arXiv preprint arXiv:2003.00982},
+  year={2020}
+}
+```
+
+
+
+
+
+
+<br><br><br>
+

configs/SBMs_node_clustering_GAT_CLUSTER.json

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+{
+    "gpu": {
+        "use": true,
+        "id": 0
+    },
+
+    "model": "GAT",
+    "dataset": "SBM_CLUSTER",
+
+    "out_dir": "out/SBMs_node_classification/",
+
+    "params": {
+        "seed": 41,
+        "epochs": 1000,
+        "batch_size": 128,
+        "init_lr": 0.001,
+        "lr_reduce_factor": 0.5,
+        "lr_schedule_patience": 5,
+        "min_lr": 1e-5,
+        "weight_decay": 0.0,
+        "print_epoch_interval": 5,
+        "max_time": 48
+    },
+
+    "net_params": {
+        "L": 4,
+        "n_heads": 8,
+        "hidden_dim": 19,
+        "out_dim": 152,
+        "residual": true,
+        "readout": "mean",
+        "in_feat_dropout": 0.0,
+        "dropout": 0.0,
+        "graph_norm": true,
+        "batch_norm": true,
+        "self_loop": false
+    }
+}

configs/SBMs_node_clustering_GAT_PATTERN.json

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+{
+    "gpu": {
+        "use": true,
+        "id": 0
+    },
+
+    "model": "GAT",
+    "dataset": "SBM_PATTERN",
+
+    "out_dir": "out/SBMs_node_classification/",
+
+    "params": {
+        "seed": 41,
+        "epochs": 1000,
+        "batch_size": 128,
+        "init_lr": 0.001,
+        "lr_reduce_factor": 0.5,
+        "lr_schedule_patience": 5,
+        "min_lr": 1e-5,
+        "weight_decay": 0.0,
+        "print_epoch_interval": 5,
+        "max_time": 48
+    },
+
+    "net_params": {
+        "L": 4,
+        "n_heads": 8,
+        "hidden_dim": 19,
+        "out_dim": 152,
+        "residual": true,
+        "readout": "mean",
+        "in_feat_dropout": 0.0,
+        "dropout": 0.0,
+        "graph_norm": true,
+        "batch_norm": true,
+        "self_loop": false
+    }
+}

configs/SBMs_node_clustering_GCN_CLUSTER.json

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+{
+    "gpu": {
+        "use": true,
+        "id": 0
+    },
+
+    "model": "GCN",
+    "dataset": "SBM_CLUSTER",
+
+    "out_dir": "out/SBMs_node_classification/",
+
+    "params": {
+        "seed": 41,
+        "epochs": 1000,
+        "batch_size": 128,
+        "init_lr": 0.001,
+        "lr_reduce_factor": 0.5,
+        "lr_schedule_patience": 5,
+        "min_lr": 1e-5,
+        "weight_decay": 0.0,
+        "print_epoch_interval": 5,
+        "max_time": 48
+    },
+
+    "net_params": {
+        "L": 4,
+        "hidden_dim": 146,
+        "out_dim": 146,
+        "residual": true,
+        "readout": "mean",
+        "in_feat_dropout": 0.0,
+        "dropout": 0.0,
+        "graph_norm": true,
+        "batch_norm": true,
+        "self_loop": false
+    }
+}

configs/SBMs_node_clustering_GCN_PATTERN.json

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+{
+    "gpu": {
+        "use": true,
+        "id": 0
+    },
+
+    "model": "GCN",
+    "dataset": "SBM_PATTERN",
+
+    "out_dir": "out/SBMs_node_classification/",
+
+    "params": {
+        "seed": 41,
+        "epochs": 1000,
+        "batch_size": 128,
+        "init_lr": 0.001,
+        "lr_reduce_factor": 0.5,
+        "lr_schedule_patience": 5,
+        "min_lr": 1e-5,
+        "weight_decay": 0.0,
+        "print_epoch_interval": 5,
+        "max_time": 48
+    },
+
+    "net_params": {
+        "L": 4,
+        "hidden_dim": 146,
+        "out_dim": 146,
+        "residual": true,
+        "readout": "mean",
+        "in_feat_dropout": 0.0,
+        "dropout": 0.0,
+        "graph_norm": true,
+        "batch_norm": true,
+        "self_loop": false
+    }
+}

configs/SBMs_node_clustering_GIN_CLUSTER.json

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+{
+    "gpu": {
+        "use": true,
+        "id": 0
+    },
+
+    "model": "GIN",
+    "dataset": "SBM_CLUSTER",
+
+    "out_dir": "out/SBMs_node_classification/",
+
+    "params": {
+        "seed": 41,
+        "epochs": 1000,
+        "batch_size": 128,
+        "init_lr": 0.001,
+        "lr_reduce_factor": 0.5,
+        "lr_schedule_patience": 5,
+        "min_lr": 1e-5,
+        "weight_decay": 0.0,
+        "print_epoch_interval": 5,
+        "max_time": 48
+    },
+
+    "net_params": {
+        "L": 4,
+        "hidden_dim": 110,
+        "residual": true,
+        "readout": "sum",
+        "n_mlp_GIN": 2,
+        "learn_eps_GIN": true,
+        "neighbor_aggr_GIN": "sum",
+        "in_feat_dropout": 0.0,
+        "dropout": 0.0,
+        "graph_norm": true,
+        "batch_norm": true
+    }
+}

configs/SBMs_node_clustering_GIN_PATTERN.json

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+{
+    "gpu": {
+        "use": true,
+        "id": 0
+    },
+
+    "model": "GIN",
+    "dataset": "SBM_PATTERN",
+
+    "out_dir": "out/SBMs_node_classification/",
+
+    "params": {
+        "seed": 41,
+        "epochs": 1000,
+        "batch_size": 128,
+        "init_lr": 0.001,
+        "lr_reduce_factor": 0.5,
+        "lr_schedule_patience": 5,
+        "min_lr": 1e-5,
+        "weight_decay": 0.0,
+        "print_epoch_interval": 5,
+        "max_time": 48
+    },
+
+    "net_params": {
+        "L": 4,
+        "hidden_dim": 110,
+        "residual": true,
+        "readout": "sum",
+        "n_mlp_GIN": 2,
+        "learn_eps_GIN": true,
+        "neighbor_aggr_GIN": "sum",
+        "in_feat_dropout": 0.0,
+        "dropout": 0.0,
+        "graph_norm": true,
+        "batch_norm": true
+    }
+}

configs/SBMs_node_clustering_GatedGCN_CLUSTER.json

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+{
+    "gpu": {
+        "use": true,
+        "id": 0
+    },
+
+    "model": "GatedGCN",
+    "dataset": "SBM_CLUSTER",
+
+    "out_dir": "out/SBMs_node_classification/",
+
+    "params": {
+        "seed": 41,
+        "epochs": 1000,
+        "batch_size": 128,
+        "init_lr": 0.001,
+        "lr_reduce_factor": 0.5,
+        "lr_schedule_patience": 5,
+        "min_lr": 1e-5,
+        "weight_decay": 0.0,
+        "print_epoch_interval": 5,
+        "max_time": 48
+    },
+
+    "net_params": {
+        "L": 4,
+        "hidden_dim": 70,
+        "out_dim": 70,
+        "residual": true,
+        "edge_feat": false,
+        "readout": "mean",
+        "in_feat_dropout": 0.0,
+        "dropout": 0.0,
+        "graph_norm": true,
+        "batch_norm": true
+    }
+}