[Model] Add c&s and SAGN, modify SIGN

cogdl/configs.py

@@ -156,6 +156,7 @@
             },
         },
         "ppnp": {
+            "general": {},
             "flickr": {
                 "lr": 0.005,
                 "weight_decay": 0.001,
@@ -209,6 +210,57 @@
                 # 96.26
             },
         },
+        "correct_smooth_mlp": {
+            "general": {},
+            "ogbn_arxiv": {
+                "correct_norm": "row",
+                "smooth_norm": "col",
+                "correct_alpha": 0.9791632871592579,
+                "smooth_alpha": 0.7564990804200602,
+                "num_correct_prop": 50,
+                "num_smooth_prop": 50,
+                "autoscale": True,
+                "norm": "batchnorm",
+            },
+            "ogbn_products": {
+                "correct_norm": "sym",
+                "smooth_norm": "row",
+                "correct_alpha": 1.0,
+                "smooth_alpha": 0.8,
+                "num_correct_prop": 50,
+                "num_smooth_prop": 50,
+                "autoscale": False,
+                "scale": 10.0,
+                "norm": "batchnorm",
+                "act_first": True,
+            },
+        },
+        "sagn": {
+            "general": {
+                "data_gpu": True,
+                "lr": 0.001,
+                "hidden-size": 512,
+                "attn-drop": 0.0,
+                "dropout": 0.7,
+            },
+            "flickr": {
+                "threshold": 0.5,
+                "label-hop": 2,
+                "weight-decay": 3e-6,
+                "nstage": [50, 50, 50],
+                "nhop": 2,
+                "batch-size": 256,
+            },
+            "reddit": {
+                "threshold": 0.9,
+                "lr": 0.0001,
+                "batch-size": 1000,
+                "nhop": 2,
+                "label-nhop": 4,
+                "weight-decay": 0.0,
+                "nstage": [500, 500, 500],
+            },
+        },
     },
     "unsupervised_node_classification": {
         "deepwalk": {

cogdl/data/data.py

@@ -196,6 +196,13 @@ def row_norm(self):
             self.normalize_adj("row")
             self.__symmetric__ = False
 
+    def col_norm(self):
+        if self.row is None:
+            self.generate_normalization("col")
+        else:
+            self.normalize_adj("col")
+            self.__symmetric__ = False
+
     def generate_normalization(self, norm="sym"):
         if self.__normed__:
             return
@@ -209,6 +216,9 @@ def generate_normalization(self, norm="sym"):
             edge_norm[torch.isinf(edge_norm)] = 0
             self.__out_norm__ = None
             self.__in_norm__ = edge_norm.view(-1, 1)
+        elif norm == "col":
+            self.row, _, _ = csr2coo(self.row_ptr, self.col, self.weight)
+            self.weight = row_normalization(self.num_nodes, self.col, self.row, self.weight)
         else:
             raise NotImplementedError
         self.__normed__ = norm
@@ -223,6 +233,8 @@ def normalize_adj(self, norm="sym"):
             self.weight = symmetric_normalization(self.num_nodes, self.row, self.col, self.weight)
         elif norm == "row":
             self.weight = row_normalization(self.num_nodes, self.row, self.col, self.weight)
+        elif norm == "col":
+            self.weight = row_normalization(self.num_nodes, self.col, self.row, self.weight)
         else:
             raise NotImplementedError
         self.__normed__ = norm
@@ -450,9 +462,16 @@ def remove_self_loops(self):
     def row_norm(self):
         self._adj.row_norm()
 
+    def col_norm(self):
+        self._adj.col_norm()
+
     def sym_norm(self):
         self._adj.sym_norm()
 
+    def normalize(self, key="sym"):
+        assert key in ["row", "sym", "col"], "Support row/col/sym normalization"
+        getattr(self, f"{key}_norm")()
+
     def is_symmetric(self):
         return self._adj.is_symmetric()
 
@@ -462,6 +481,28 @@ def set_symmetric(self):
     def set_asymmetric(self):
         self._adj.set_symmetric(False)
 
+    def is_inductive(self):
+        return self._adj_train is not None
+
+    def mask2nid(self, split):
+        mask = getattr(self, f"{split}_mask")
+        if mask is not None:
+            if mask.dtype is torch.bool:
+                return torch.where(mask)[0]
+            return mask
+
+    @property
+    def train_nid(self):
+        return self.mask2nid("train")
+
+    @property
+    def val_nid(self):
+        return self.mask2nid("val")
+
+    @property
+    def test_nid(self):
+        return self.mask2nid("test")
+
     @contextmanager
     def local_graph(self, key=None):
         self.__temp_adj_stack__.append(self._adj)

cogdl/datasets/ogb.py

@@ -84,6 +84,12 @@ class OGBArxivDataset(OGBNDataset):
     def __init__(self, data_path="data"):
         dataset = "ogbn-arxiv"
         super(OGBArxivDataset, self).__init__(data_path, dataset)
+        self.preprocessing()
+
+    def preprocessing(self):
+        row, col = self.data.edge_index
+        edge_index = to_undirected(torch.stack([row, col]))
+        self.data.edge_index = edge_index
 
     def get_evaluator(self):
         evaluator = NodeEvaluator(name="ogbn-arxiv")

cogdl/match.yml

@@ -34,6 +34,8 @@ node_classification:
     - mvgrl
     - grace
     - self_auxiliary_task
+    - correct_smooth_mlp
+    - sagn
     dataset:
     - cora
     - citeseer

cogdl/models/__init__.py

@@ -130,4 +130,7 @@ def build_model(args):
     "self_auxiliary_task": "cogdl.models.nn.self_auxiliary_task",
     "moe_gcn": "cogdl.models.nn.moe_gcn",
     "lightgcn": "cogdl.models.nn.lightgcn",
+    "correct_smooth": "cogdl.models.nn.correct_smooth",
+    "correct_smooth_mlp": "cogdl.models.nn.correct_smooth",
+    "sagn": "cogdl.models.nn.sagn",
 }

cogdl/models/base_model.py

@@ -42,7 +42,7 @@ def graph_classification_loss(self, batch):
         return self.loss_fn(pred, batch.y)
 
     @staticmethod
-    def get_trainer(task: Any, args: Any) -> Optional[Type[BaseTrainer]]:
+    def get_trainer(args=None) -> Optional[Type[BaseTrainer]]:
         return None
 
     def set_device(self, device):

cogdl/models/nn/agc.py

@@ -28,7 +28,8 @@ def __init__(self, num_clusters, max_iter):
         self.k = 0
         self.features_matrix = None
 
-    def get_trainer(self, task, args):
+    @staticmethod
+    def get_trainer(args):
         return AGCTrainer
 
     def get_features(self, data):