version2 update

.gitignore

@@ -25,4 +25,13 @@ data/TSP/*.pkl
 data/TSP/*.zip
 data/TSP/pyconcorde/
 
+#COLLAB
+data/COLLAB/*
+dataset/
+
+#CSL
+data/CSL/*.pkl
+data/CSL/*.zip
+data/CSL/*.pt
+
 

README.md

@@ -32,17 +32,22 @@
 
 <br>
 
-## 5. Adding a new graph neural network
+## 5. Adding a Message-passing GCN
 
-[Step-by-step directions](./docs/05_add_gnn.md) to add a GNN to the benchmark.
+[Step-by-step directions](./docs/05_add_mpgcn.md) to add a MP-GCN to the benchmark.
 
 
+<br>
+
+## 6. Adding a Weisfeiler-Lehman GNN
+
+[Step-by-step directions](./docs/06_add_wlgnn.md) to add a WL-GNN to the benchmark.
 
 
 
 <br>
 
-## 6. Reference 
+## 7. Reference 
 
 ```
 @article{dwivedi2020benchmarkgnns,

configs/COLLAB_edge_classification_GAT_40k.json

@@ -0,0 +1,38 @@
+{
+    "gpu": {
+        "use": true,
+        "id": 1
+    },
+
+    "model": "GAT",
+    "dataset": "OGBL-COLLAB",
+
+    "out_dir": "out/COLLAB_edge_classification/GAT/",
+
+    "params": {
+        "seed": 41,
+        "epochs": 500,
+        "batch_size": 32768,
+        "init_lr": 0.001,
+        "lr_reduce_factor": 0.5,
+        "lr_schedule_patience": 10,
+        "min_lr": 1e-5,
+        "weight_decay": 0.0,
+        "print_epoch_interval": 1,
+        "max_time": 12
+    },
+
+    "net_params": {
+        "L": 3,
+        "hidden_dim": 19,
+        "out_dim": 76,
+        "residual": true,
+        "readout": "mean",
+        "n_heads": 4,
+        "in_feat_dropout": 0.0,
+        "dropout": 0.0,
+        "batch_norm": true,
+        "self_loop": false,
+        "layer_type": "dgl"
+    }
+}

configs/COLLAB_edge_classification_GAT_edgefeat.json

@@ -0,0 +1,39 @@
+{
+    "gpu": {
+        "use": true,
+        "id": 1
+    },
+
+    "model": "GAT",
+    "dataset": "OGBL-COLLAB",
+
+    "out_dir": "out/COLLAB_edge_classification/GAT/edgefeat/",
+
+    "params": {
+        "seed": 41,
+        "epochs": 500,
+        "batch_size": 32768,
+        "init_lr": 0.001,
+        "lr_reduce_factor": 0.5,
+        "lr_schedule_patience": 10,
+        "min_lr": 1e-5,
+        "weight_decay": 0.0,
+        "print_epoch_interval": 1,
+        "max_time": 24
+    },
+
+    "net_params": {
+        "L": 3,
+        "hidden_dim": 20,
+        "out_dim": 60,
+        "residual": true,
+        "readout": "mean",
+        "n_heads": 3,
+        "in_feat_dropout": 0.0,
+        "dropout": 0.0,
+        "batch_norm": true,
+        "self_loop": false,
+        "edge_feat": false,
+        "layer_type": "edgefeat"
+    }
+}

configs/COLLAB_edge_classification_GAT_edgereprfeat.json

@@ -0,0 +1,39 @@
+{
+    "gpu": {
+        "use": true,
+        "id": 1
+    },
+
+    "model": "GAT",
+    "dataset": "OGBL-COLLAB",
+
+    "out_dir": "out/COLLAB_edge_classification/GAT/edgereprfeat/",
+
+    "params": {
+        "seed": 41,
+        "epochs": 500,
+        "batch_size": 32768,
+        "init_lr": 0.001,
+        "lr_reduce_factor": 0.5,
+        "lr_schedule_patience": 10,
+        "min_lr": 1e-5,
+        "weight_decay": 0.0,
+        "print_epoch_interval": 1,
+        "max_time": 24
+    },
+
+    "net_params": {
+        "L": 3,
+        "hidden_dim": 13,
+        "out_dim": 39,
+        "residual": true,
+        "readout": "mean",
+        "n_heads": 3,
+        "in_feat_dropout": 0.0,
+        "dropout": 0.0,
+        "batch_norm": true,
+        "self_loop": false,
+        "edge_feat": false,
+        "layer_type": "edgereprfeat"
+    }
+}

configs/COLLAB_edge_classification_GAT_isotropic.json

@@ -0,0 +1,39 @@
+{
+    "gpu": {
+        "use": true,
+        "id": 1
+    },
+
+    "model": "GAT",
+    "dataset": "OGBL-COLLAB",
+
+    "out_dir": "out/COLLAB_edge_classification/GAT/isotropic/",
+
+    "params": {
+        "seed": 41,
+        "epochs": 500,
+        "batch_size": 32768,
+        "init_lr": 0.001,
+        "lr_reduce_factor": 0.5,
+        "lr_schedule_patience": 10,
+        "min_lr": 1e-5,
+        "weight_decay": 0.0,
+        "print_epoch_interval": 1,
+        "max_time": 24
+    },
+
+    "net_params": {
+        "L": 3,
+        "hidden_dim": 20,
+        "out_dim": 60,
+        "residual": true,
+        "readout": "mean",
+        "n_heads": 3,
+        "in_feat_dropout": 0.0,
+        "dropout": 0.0,
+        "batch_norm": true,
+        "self_loop": false,
+        "edge_feat": false,
+        "layer_type": "isotropic"
+    }
+}

configs/COLLAB_edge_classification_GCN_40k.json

@@ -0,0 +1,36 @@
+{
+    "gpu": {
+        "use": true,
+        "id": 1
+    },
+
+    "model": "GCN",
+    "dataset": "OGBL-COLLAB",
+
+    "out_dir": "out/COLLAB_edge_classification/GCN/",
+
+    "params": {
+        "seed": 41,
+        "epochs": 500,
+        "batch_size": 32768,
+        "init_lr": 0.001,
+        "lr_reduce_factor": 0.5,
+        "lr_schedule_patience": 10,
+        "min_lr": 1e-5,
+        "weight_decay": 0.0,
+        "print_epoch_interval": 1,
+        "max_time": 12
+    },
+
+    "net_params": {
+        "L": 3,
+        "hidden_dim": 74,
+        "out_dim": 74,
+        "residual": true,
+        "readout": "mean",
+        "in_feat_dropout": 0.0,
+        "dropout": 0.0,
+        "batch_norm": true,
+        "self_loop": false
+    }
+}

configs/COLLAB_edge_classification_GIN_40k.json

@@ -0,0 +1,37 @@
+{
+    "gpu": {
+        "use": true,
+        "id": 1
+    },
+
+    "model": "GIN",
+    "dataset": "OGBL-COLLAB",
+
+    "out_dir": "out/COLLAB_edge_classification/GIN/",
+
+    "params": {
+        "seed": 41,
+        "epochs": 500,
+        "batch_size": 32768,
+        "init_lr": 0.001,
+        "lr_reduce_factor": 0.5,
+        "lr_schedule_patience": 10,
+        "min_lr": 1e-5,
+        "weight_decay": 0.0,
+        "print_epoch_interval": 1,
+        "max_time": 12
+    },
+
+    "net_params": {
+        "L": 3,
+        "hidden_dim": 60,
+        "residual": true,
+        "readout": "sum",
+        "n_mlp_GIN": 2,
+        "learn_eps_GIN": true,
+        "neighbor_aggr_GIN": "sum",
+        "in_feat_dropout": 0.0,
+        "dropout": 0.0,
+        "batch_norm": true
+    }
+}

configs/COLLAB_edge_classification_GatedGCN_40k.json

@@ -0,0 +1,38 @@
+{
+    "gpu": {
+        "use": true,
+        "id": 3
+    },
+
+    "model": "GatedGCN",
+    "dataset": "OGBL-COLLAB",
+
+    "out_dir": "out/COLLAB_edge_classification/GatedGCN/",
+
+    "params": {
+        "seed": 41,
+        "epochs": 500,
+        "batch_size": 32768,
+        "init_lr": 0.001,
+        "lr_reduce_factor": 0.5,
+        "lr_schedule_patience": 10,
+        "min_lr": 1e-5,
+        "weight_decay": 0.0,
+        "print_epoch_interval": 1,
+        "max_time": 12
+    },
+
+    "net_params": {
+        "L": 3,
+        "hidden_dim": 44,
+        "out_dim": 44,
+        "residual": true,
+        "edge_feat": false,
+        "readout": "mean",
+        "in_feat_dropout": 0.0,
+        "dropout": 0.0,
+        "batch_norm": true,
+        "layer_type": "edgereprfeat",
+        "pos_enc": false
+    }
+}

configs/COLLAB_edge_classification_GatedGCN_PE_40k.json

@@ -0,0 +1,39 @@
+{
+    "gpu": {
+        "use": true,
+        "id": 3
+    },
+
+    "model": "GatedGCN",
+    "dataset": "OGBL-COLLAB",
+
+    "out_dir": "out/COLLAB_edge_classification/GatedGCN/",
+
+    "params": {
+        "seed": 41,
+        "epochs": 500,
+        "batch_size": 32768,
+        "init_lr": 0.001,
+        "lr_reduce_factor": 0.5,
+        "lr_schedule_patience": 10,
+        "min_lr": 1e-5,
+        "weight_decay": 0.0,
+        "print_epoch_interval": 1,
+        "max_time": 12
+    },
+
+    "net_params": {
+        "L": 3, 
+        "hidden_dim": 44,
+        "out_dim": 44,
+        "residual": true,
+        "edge_feat": false,
+        "readout": "mean",
+        "in_feat_dropout": 0.0,
+        "dropout": 0.0,
+        "batch_norm": true,
+        "layer_type": "edgereprfeat",
+        "pos_enc": true,
+        "pos_enc_dim": 40
+    }
+}