diff --git a/autogl/data/graph/_general_static_graph/utils/conversion/__init__.py b/autogl/data/graph/_general_static_graph/utils/conversion/__init__.py
index ec0f06e..2ed61c2 100644
--- a/autogl/data/graph/_general_static_graph/utils/conversion/__init__.py
+++ b/autogl/data/graph/_general_static_graph/utils/conversion/__init__.py
@@ -1,3 +1,4 @@
+from ._general import StaticGraphToGeneralData, static_graph_to_general_data
 from ._nx import (
     HomogeneousStaticGraphToNetworkX
 )
diff --git a/autogl/data/graph/_general_static_graph/utils/conversion/_general.py b/autogl/data/graph/_general_static_graph/utils/conversion/_general.py
new file mode 100644
index 0000000..647085b
--- /dev/null
+++ b/autogl/data/graph/_general_static_graph/utils/conversion/_general.py
@@ -0,0 +1,79 @@
+import torch
+import typing as _typing
+import autogl
+from ... import GeneralStaticGraph
+
+
+class StaticGraphToGeneralData:
+    def __init__(self, *__args, **__kwargs):
+        pass
+
+    def __call__(
+            self, static_graph: GeneralStaticGraph,
+            *__args, **__kwargs
+    ):
+        if not isinstance(static_graph, GeneralStaticGraph):
+            raise TypeError
+        elif not static_graph.nodes.is_homogeneous:
+            raise ValueError("Provided static graph MUST consist of homogeneous nodes")
+        homogeneous_node_type: _typing.Optional[str] = (
+            list(static_graph.nodes)[0]
+            if len(list(static_graph.nodes)) > 0 else None
+        )
+        data: _typing.Dict[str, torch.Tensor] = dict()
+        if isinstance(homogeneous_node_type, str):
+            node_and_edge_data_keys_intersection: _typing.Set[str] = (
+                    set(static_graph.nodes.data) & set(static_graph.data)
+            )
+            if len(node_and_edge_data_keys_intersection) > 0:
+                raise ValueError(
+                    f"Provided static graph contains duplicate data "
+                    f"with same keys {node_and_edge_data_keys_intersection}"
+                    f"for homogeneous nodes data and graph-level data, "
+                    f"please refer to doc for more details."
+                )
+            data.update(static_graph.nodes.data)
+            data.update(static_graph.data)
+        else:
+            data.update(static_graph.data)
+
+        if len(list(static_graph.edges)) == 1:
+            data["edge_index"] = static_graph.edges.connections
+            if len(set(data.keys()) & set(static_graph.edges.data.keys())) > 0:
+                raise ValueError(
+                    "Provided static graph contains duplicate data with same key, "
+                    "please refer to doc for more details."
+                )
+            data.update(static_graph.edges.data)
+        elif len(list(static_graph.edges)) > 1:
+            for canonical_edge_type in static_graph.edges:
+                if homogeneous_node_type is not None and isinstance(homogeneous_node_type, str) and (
+                        canonical_edge_type.source_node_type != homogeneous_node_type or
+                        canonical_edge_type.target_node_type != homogeneous_node_type
+                ):
+                    continue
+                if len(canonical_edge_type.relation_type) < 4 or canonical_edge_type[-4:] != 'edge':
+                    continue
+                data[f"{canonical_edge_type.relation_type}_index"] = (
+                    static_graph.edges[canonical_edge_type].connections
+                )
+
+                edge_type_prefix: str = canonical_edge_type.relation_type[:-4]
+                for data_key in static_graph.edges[canonical_edge_type].data:
+                    if len(data_key) >= 4 and data_key[:4] == 'edge':
+                        data[f"{edge_type_prefix}{data_key}"] = (
+                            static_graph.edges[canonical_edge_type].data[data_key].detach()
+                        )
+                    else:
+                        data[f"{canonical_edge_type.relation_type}_{data_key}"] = (
+                            static_graph.edges[canonical_edge_type].data[data_key].detach()
+                        )
+
+        general_data = autogl.data.Data()
+        for key, value in data.items():
+            setattr(general_data, key, value)
+        return general_data
+
+
+def static_graph_to_general_data(static_graph: GeneralStaticGraph) -> autogl.data.Data:
+    return StaticGraphToGeneralData().__call__(static_graph)
diff --git a/autogl/datasets/utils/__init__.py b/autogl/datasets/utils/__init__.py
index 85b58ff..a7e80d0 100644
--- a/autogl/datasets/utils/__init__.py
+++ b/autogl/datasets/utils/__init__.py
@@ -1,6 +1,6 @@
+from ._split_edges import split_edges
 from ._general import (
     index_to_mask,
-    split_edges,
     random_splits_mask,
     random_splits_mask_class,
     graph_cross_validation,
diff --git a/autogl/datasets/utils/_general.py b/autogl/datasets/utils/_general.py
index a3e5b30..77d878f 100644
--- a/autogl/datasets/utils/_general.py
+++ b/autogl/datasets/utils/_general.py
@@ -5,9 +5,7 @@ import torch.utils.data
 import typing as _typing
 from sklearn.model_selection import StratifiedKFold, KFold
 from autogl import backend as _backend
-from autogl.data import Data, Dataset, InMemoryStaticGraphSet
-from ...data.graph import GeneralStaticGraph, GeneralStaticGraphGenerator
-from . import _pyg
+from autogl.data import InMemoryStaticGraphSet
 
 
 def index_to_mask(index: torch.Tensor, size):
@@ -16,70 +14,6 @@ def index_to_mask(index: torch.Tensor, size):
     return mask
 
 
-def split_edges(
-        dataset: InMemoryStaticGraphSet,
-        train_ratio: float, val_ratio: float
-) -> InMemoryStaticGraphSet:
-    test_ratio: float = 1 - train_ratio - val_ratio
-
-    def _split_edges_for_graph(homogeneous_static_graph: GeneralStaticGraph) -> GeneralStaticGraph:
-        if not isinstance(homogeneous_static_graph, GeneralStaticGraph):
-            raise TypeError
-        elif not homogeneous_static_graph.edges.is_homogeneous:
-            raise ValueError("The provided graph MUST consist of homogeneous edges.")
-        else:
-            split_data = _pyg.train_test_split_edges(
-                Data(
-                    edge_index=homogeneous_static_graph.edges.connections.detach().clone(),
-                    edge_attr=(
-                        homogeneous_static_graph.edges.data['edge_attr'].detach().clone()
-                        if 'edge_attr' in homogeneous_static_graph.edges.data else None
-                    )
-                ),
-                val_ratio, test_ratio
-            )
-            original_edge_type = [et for et in homogeneous_static_graph.edges][0]
-
-            split_static_graph = GeneralStaticGraphGenerator.create_heterogeneous_static_graph(
-                dict([
-                    (node_type, homogeneous_static_graph.nodes[node_type].data)
-                    for node_type in homogeneous_static_graph.nodes
-                ]),
-                {
-                    (original_edge_type.source_node_type, "train_pos_edge", original_edge_type.target_node_type): (
-                        getattr(split_data, "train_pos_edge_index"),
-                        {"edge_attr": getattr(split_data, "train_pos_edge_attr")}
-                        if isinstance(getattr(split_data, "train_pos_edge_attr"), torch.Tensor)
-                        else None
-                    ),
-                    (original_edge_type.source_node_type, "val_pos_edge", original_edge_type.target_node_type): (
-                        getattr(split_data, "val_pos_edge_index"),
-                        {"edge_attr": getattr(split_data, "val_pos_edge_attr")}
-                        if isinstance(getattr(split_data, "val_pos_edge_attr"), torch.Tensor)
-                        else None
-                    ),
-                    (original_edge_type.source_node_type, "val_neg_edge", original_edge_type.target_node_type):
-                        getattr(split_data, "val_neg_edge_index"),
-                    (original_edge_type.source_node_type, "test_pos_edge", original_edge_type.target_node_type): (
-                        getattr(split_data, "test_pos_edge_index"),
-                        {"edge_attr": getattr(split_data, "test_pos_edge_attr")}
-                        if isinstance(getattr(split_data, "test_pos_edge_attr"), torch.Tensor)
-                        else None
-                    ),
-                    (original_edge_type.source_node_type, "test_neg_edge", original_edge_type.target_node_type):
-                        getattr(split_data, "test_neg_edge_index")
-                },
-                homogeneous_static_graph.data
-            )
-            return split_static_graph
-
-    if not isinstance(dataset, InMemoryStaticGraphSet):
-        raise TypeError
-    for index in range(len(dataset)):
-        dataset[index] = _split_edges_for_graph(dataset[index])
-    return dataset
-
-
 def random_splits_mask(
         dataset: InMemoryStaticGraphSet,
         train_ratio: float = 0.2, val_ratio: float = 0.4,
diff --git a/autogl/datasets/utils/_split_edges/__init__.py b/autogl/datasets/utils/_split_edges/__init__.py
new file mode 100644
index 0000000..d29abab
--- /dev/null
+++ b/autogl/datasets/utils/_split_edges/__init__.py
@@ -0,0 +1 @@
+from .split_edges import split_edges
diff --git a/autogl/datasets/utils/_split_edges/_dgl_compatible.py b/autogl/datasets/utils/_split_edges/_dgl_compatible.py
new file mode 100644
index 0000000..40b2bb7
--- /dev/null
+++ b/autogl/datasets/utils/_split_edges/_dgl_compatible.py
@@ -0,0 +1,207 @@
+import numpy as np
+import scipy.sparse as sp
+import torch
+import typing as _typing
+import dgl
+import autogl.data.graph
+from .train_test_split_edges import train_test_split_edges
+from autogl.data.graph.utils.conversion import static_graph_to_general_data
+
+
+class _SplitEdgesDGLImpl:
+    @classmethod
+    def __split_edges_train_val_test(
+            cls, g: dgl.DGLGraph,
+            train_ratio: float, val_ratio: float
+    ) -> _typing.Tuple[
+        dgl.DGLGraph, dgl.DGLGraph, dgl.DGLGraph,
+        dgl.DGLGraph, dgl.DGLGraph, dgl.DGLGraph, dgl.DGLGraph
+    ]:
+        u, v = g.edges()
+
+        eids = np.arange(g.number_of_edges())
+        eids = np.random.permutation(eids)
+
+        valid_size = int(len(eids) * val_ratio)
+        test_size = int(len(eids) * (1 - train_ratio - val_ratio))
+        train_size = g.number_of_edges() - test_size - valid_size
+
+        test_pos_u, test_pos_v = u[eids[:test_size]], v[eids[:test_size]]
+        valid_pos_u, valid_pos_v = u[eids[test_size:test_size + valid_size]], v[eids[test_size:test_size + valid_size]]
+        train_pos_u, train_pos_v = u[eids[test_size + valid_size:]], v[eids[test_size + valid_size:]]
+
+        # Find all negative edges and split them for training and testing
+        adj = sp.coo_matrix((np.ones(len(u)), (u.numpy(), v.numpy())))
+        adj_neg = 1 - adj.todense() - np.eye(g.number_of_nodes())
+        neg_u, neg_v = np.where(adj_neg != 0)
+
+        neg_eids = np.random.choice(len(neg_u), g.number_of_edges())
+        test_neg_u, test_neg_v = neg_u[neg_eids[:test_size]], neg_v[neg_eids[:test_size]]
+        valid_neg_u, valid_neg_v = neg_u[neg_eids[test_size:test_size + valid_size]], neg_v[neg_eids[test_size:test_size + valid_size]]
+        train_neg_u, train_neg_v = neg_u[neg_eids[train_size:]], neg_v[neg_eids[train_size:]]
+
+        train_g = dgl.remove_edges(g, eids[:test_size + valid_size])
+
+        train_pos_g = dgl.graph((train_pos_u, train_pos_v), num_nodes=g.number_of_nodes())
+        train_neg_g = dgl.graph((train_neg_u, train_neg_v), num_nodes=g.number_of_nodes())
+
+        valid_pos_g = dgl.graph((valid_pos_u, valid_pos_v), num_nodes=g.number_of_nodes())
+        valid_neg_g = dgl.graph((valid_neg_u, valid_neg_v), num_nodes=g.number_of_nodes())
+
+        test_pos_g = dgl.graph((test_pos_u, test_pos_v), num_nodes=g.number_of_nodes())
+        test_neg_g = dgl.graph((test_neg_u, test_neg_v), num_nodes=g.number_of_nodes())
+
+        return (
+            train_g, train_pos_g, train_neg_g,
+            valid_pos_g, valid_neg_g, test_pos_g, test_neg_g
+        )
+
+    @classmethod
+    def __split_edges_train_test(
+            cls, g: dgl.DGLGraph, train_ratio: float
+    ) -> _typing.Tuple[
+        dgl.DGLGraph, dgl.DGLGraph, dgl.DGLGraph,
+        dgl.DGLGraph, dgl.DGLGraph,
+    ]:
+        u, v = g.edges()
+
+        eids = np.arange(g.number_of_edges())
+        eids = np.random.permutation(eids)
+        test_size = int(len(eids) * (1 - train_ratio))
+        train_size = g.number_of_edges() - test_size
+        test_pos_u, test_pos_v = u[eids[:test_size]], v[eids[:test_size]]
+        train_pos_u, train_pos_v = u[eids[test_size:]], v[eids[test_size:]]
+
+        # Find all negative edges and split them for training and testing
+        adj = sp.coo_matrix((np.ones(len(u)), (u.numpy(), v.numpy())))
+        adj_neg = 1 - adj.todense() - np.eye(g.number_of_nodes())
+        neg_u, neg_v = np.where(adj_neg != 0)
+
+        neg_eids = np.random.choice(len(neg_u), g.number_of_edges())
+        test_neg_u, test_neg_v = neg_u[neg_eids[:test_size]], neg_v[neg_eids[:test_size]]
+        train_neg_u, train_neg_v = neg_u[neg_eids[train_size:]], neg_v[neg_eids[train_size:]]
+
+        train_g = dgl.remove_edges(g, eids[:test_size])
+
+        train_pos_g = dgl.graph((train_pos_u, train_pos_v), num_nodes=g.number_of_nodes())
+        train_neg_g = dgl.graph((train_neg_u, train_neg_v), num_nodes=g.number_of_nodes())
+
+        test_pos_g = dgl.graph((test_pos_u, test_pos_v), num_nodes=g.number_of_nodes())
+        test_neg_g = dgl.graph((test_neg_u, test_neg_v), num_nodes=g.number_of_nodes())
+
+        return train_g, train_pos_g, train_neg_g, test_pos_g, test_neg_g
+
+    @classmethod
+    def split_edges_for_dgl_graph(
+            cls, graph: dgl.DGLGraph,
+            train_ratio: float, val_ratio: _typing.Optional[float] = ...
+    ) -> _typing.Union[
+        _typing.Tuple[
+            dgl.DGLGraph, dgl.DGLGraph,
+            dgl.DGLGraph, dgl.DGLGraph,
+            dgl.DGLGraph, dgl.DGLGraph,
+            dgl.DGLGraph
+        ],
+        _typing.Tuple[
+            dgl.DGLGraph, dgl.DGLGraph, dgl.DGLGraph,
+            dgl.DGLGraph, dgl.DGLGraph,
+        ]
+    ]:
+        if not 0 < train_ratio < 1:
+            raise ValueError(f"Invalid train_ratio as {train_ratio}")
+        if isinstance(val_ratio, float):
+            if not 0 < val_ratio < 1:
+                raise ValueError(f"Invalid val_ratio as {val_ratio}")
+            if not 0 < train_ratio + val_ratio < 1:
+                raise ValueError(
+                    f"Invalid combination (train_ratio, val_ratio) "
+                    f"as ({train_ratio}, {val_ratio})"
+                )
+            return cls.__split_edges_train_val_test(graph, train_ratio, val_ratio)
+        else:
+            return cls.__split_edges_train_test(graph, train_ratio)
+
+
+def split_edges_for_data(
+        data: _typing.Union[
+            dgl.DGLGraph, autogl.data.graph.GeneralStaticGraph, _typing.Any
+        ],
+        train_ratio: float, val_ratio: _typing.Optional[float]
+) -> _typing.Union[
+    autogl.data.Data,
+    _typing.Tuple[
+        dgl.DGLGraph, dgl.DGLGraph,
+        dgl.DGLGraph, dgl.DGLGraph,
+        dgl.DGLGraph, dgl.DGLGraph,
+        dgl.DGLGraph
+    ],
+    _typing.Tuple[
+        dgl.DGLGraph, dgl.DGLGraph, dgl.DGLGraph,
+        dgl.DGLGraph, dgl.DGLGraph,
+    ]
+]:
+    if isinstance(data, dgl.DGLGraph):
+        if not data.is_homogeneous:
+            raise ValueError(
+                "provided DGL graph to split edges MUST be homogeneous"
+            )
+        else:
+            return _SplitEdgesDGLImpl.split_edges_for_dgl_graph(
+                data, train_ratio, val_ratio
+            )
+    elif isinstance(data, autogl.data.graph.GeneralStaticGraph):
+        if not (data.nodes.is_homogeneous and data.edges.is_homogeneous):
+            raise ValueError(
+                "Provided instance of GeneralStaticGraph MUST be homogeneous"
+            )
+        __data = static_graph_to_general_data(data)
+        edge_index: torch.LongTensor = data.edges.connections
+        edge_attr: _typing.Optional[torch.Tensor] = (
+            data.edges.data['edge_attr'] if 'edge_attr' in data.edges.data else None
+        )
+    elif (
+            hasattr(data, 'edge_index') and
+            isinstance(data.edge_index, torch.Tensor) and
+            data.edge_index.dim() == data.edge_index.size(0) == 2
+    ):
+        edge_index: torch.LongTensor = data.edge_index
+        if (
+                hasattr(data, 'edge_attr') and
+                isinstance(data.edge_attr, torch.Tensor) and
+                data.edge_attr.size(0) == edge_index.size(1)
+        ):
+            edge_attr: _typing.Optional[torch.Tensor] = data.edge_attr
+        else:
+            edge_attr: _typing.Optional[torch.Tensor] = None
+        if hasattr(data, 'x') and isinstance(data.x, torch.Tensor):
+            x: _typing.Optional[torch.Tensor] = data.x
+        else:
+            x: _typing.Optional[torch.Tensor] = None
+        if hasattr(data, 'y') and isinstance(data.y, torch.Tensor):
+            y: _typing.Optional[torch.Tensor] = data.x
+        else:
+            y: _typing.Optional[torch.Tensor] = None
+        __data = autogl.data.Data(
+            edge_index=edge_index, edge_attr=edge_attr, x=x, y=y
+        )
+    else:
+        raise ValueError
+
+    if isinstance(val_ratio, float) and 0 < val_ratio < 1:
+        test_ratio = 1 - train_ratio - val_ratio
+    else:
+        test_ratio = 1 - train_ratio
+    compound_results = train_test_split_edges(
+        edge_index, edge_attr,
+        val_ratio=val_ratio, test_ratio=test_ratio
+    )
+    __data.train_pos_edge_index = compound_results.train_pos_edge_index
+    __data.train_pos_edge_attr = compound_results.train_pos_edge_attr
+    __data.train_neg_adj_mask = compound_results.train_neg_adj_mask
+    __data.val_pos_edge_index = compound_results.val_pos_edge_index
+    __data.val_pos_edge_attr = compound_results.val_pos_edge_attr
+    __data.val_neg_edge_index = compound_results.val_neg_edge_index
+    __data.test_pos_edge_index = compound_results.test_pos_edge_index
+    __data.test_pos_edge_attr = compound_results.test_pos_edge_attr
+    __data.test_neg_edge_index = compound_results.test_neg_edge_index
+    return __data
diff --git a/autogl/datasets/utils/_split_edges/_pyg_compatible.py b/autogl/datasets/utils/_split_edges/_pyg_compatible.py
new file mode 100644
index 0000000..83ec892
--- /dev/null
+++ b/autogl/datasets/utils/_split_edges/_pyg_compatible.py
@@ -0,0 +1,71 @@
+import torch
+import typing as _typing
+import torch_geometric
+import autogl
+from autogl.data.graph import GeneralStaticGraph
+from autogl.data.graph.utils.conversion import static_graph_to_pyg_data
+from .train_test_split_edges import train_test_split_edges
+
+
+def split_edges_for_data(
+        data: _typing.Union[
+            torch_geometric.data.Data, autogl.data.graph.GeneralStaticGraph, _typing.Any
+        ],
+        train_ratio: float, val_ratio: float
+) -> torch_geometric.data.Data:
+    if isinstance(data, autogl.data.graph.GeneralStaticGraph):
+        if not (data.nodes.is_homogeneous and data.edges.is_homogeneous):
+            raise ValueError(
+                "Provided instance of GeneralStaticGraph MUST be homogeneous"
+            )
+        edge_index: torch.LongTensor = data.edges.connections
+        edge_attr: _typing.Optional[torch.Tensor] = (
+            data.edges.data['edge_attr'] if 'edge_attr' in data.edges.data else None
+        )
+        __data = static_graph_to_pyg_data(data)
+    elif (
+            hasattr(data, 'edge_index') and
+            isinstance(data.edge_index, torch.Tensor) and
+            data.edge_index.dim() == data.edge_index.size(0) == 2
+    ):
+        edge_index: torch.LongTensor = data.edge_index
+        if (
+                hasattr(data, 'edge_attr') and
+                isinstance(data.edge_attr, torch.Tensor) and
+                data.edge_attr.size(0) == edge_index.size(1)
+        ):
+            edge_attr: _typing.Optional[torch.Tensor] = data.edge_attr
+        else:
+            edge_attr: _typing.Optional[torch.Tensor] = None
+        if hasattr(data, 'x') and isinstance(data.x, torch.Tensor):
+            x: _typing.Optional[torch.Tensor] = data.x
+        else:
+            x: _typing.Optional[torch.Tensor] = None
+        if hasattr(data, 'y') and isinstance(data.y, torch.Tensor):
+            y: _typing.Optional[torch.Tensor] = data.x
+        else:
+            y: _typing.Optional[torch.Tensor] = None
+        __data = torch_geometric.data.Data(
+            edge_index=edge_index, edge_attr=edge_attr, x=x, y=y
+        )
+    else:
+        raise ValueError
+
+    if isinstance(val_ratio, float) and 0 < val_ratio < 1:
+        test_ratio = 1 - train_ratio - val_ratio
+    else:
+        test_ratio = 1 - train_ratio
+    compound_results = train_test_split_edges(
+        edge_index, edge_attr,
+        val_ratio=val_ratio, test_ratio=test_ratio
+    )
+    __data.train_pos_edge_index = compound_results.train_pos_edge_index
+    __data.train_pos_edge_attr = compound_results.train_pos_edge_attr
+    __data.train_neg_adj_mask = compound_results.train_neg_adj_mask
+    __data.val_pos_edge_index = compound_results.val_pos_edge_index
+    __data.val_pos_edge_attr = compound_results.val_pos_edge_attr
+    __data.val_neg_edge_index = compound_results.val_neg_edge_index
+    __data.test_pos_edge_index = compound_results.test_pos_edge_index
+    __data.test_pos_edge_attr = compound_results.test_pos_edge_attr
+    __data.test_neg_edge_index = compound_results.test_neg_edge_index
+    return __data
diff --git a/autogl/datasets/utils/_split_edges/split_edges.py b/autogl/datasets/utils/_split_edges/split_edges.py
new file mode 100644
index 0000000..dc5a5c1
--- /dev/null
+++ b/autogl/datasets/utils/_split_edges/split_edges.py
@@ -0,0 +1,32 @@
+import typing as _typing
+from autogl.data import InMemoryDataset
+import autogl
+
+if autogl.backend.DependentBackend.is_dgl():
+    from ._dgl_compatible import split_edges_for_data
+elif autogl.backend.DependentBackend.is_pyg():
+    from ._pyg_compatible import split_edges_for_data
+else:
+    raise NotImplementedError
+
+
+def split_edges(
+        dataset: InMemoryDataset,
+        train_ratio: float, val_ratio: _typing.Optional[float]
+) -> InMemoryDataset:
+    if isinstance(val_ratio, float) and not 0 < train_ratio + val_ratio < 1:
+        raise ValueError
+    elif not 0 < train_ratio < 1:
+        raise ValueError
+    if (
+            autogl.backend.DependentBackend.is_pyg() and
+            not (isinstance(val_ratio, float) and 0 < val_ratio < 1)
+    ):
+        raise ValueError(
+            "For PyG as backend, val_ratio MUST be specific float between 0 and 1, "
+            "i.e. 0 < val_ratio < 1"
+        )
+    return InMemoryDataset(
+        [split_edges_for_data(item, train_ratio, val_ratio) for item in dataset],
+        dataset.train_index, dataset.val_index, dataset.test_index
+    )
diff --git a/autogl/datasets/utils/_split_edges/train_test_split_edges.py b/autogl/datasets/utils/_split_edges/train_test_split_edges.py
new file mode 100644
index 0000000..1e85371
--- /dev/null
+++ b/autogl/datasets/utils/_split_edges/train_test_split_edges.py
@@ -0,0 +1,310 @@
+import math
+import torch
+import typing as _typing
+
+
+def _maybe_num_nodes(edge_index, num_nodes=None):
+    if isinstance(num_nodes, int):
+        return num_nodes
+    elif isinstance(edge_index, torch.Tensor):
+        return int(edge_index.max()) + 1 if edge_index.numel() > 0 else 0
+    else:
+        return max(edge_index.size(0), edge_index.size(1))
+
+
+def __coalesce(
+        edge_index: torch.Tensor,
+        edge_attr: _typing.Union[
+            torch.Tensor, _typing.Iterable[torch.Tensor], None
+        ] = None,
+        num_nodes: _typing.Optional[int] = ...,
+        is_sorted: bool = False,
+        sort_by_row: bool = True
+) -> _typing.Union[
+    torch.Tensor, _typing.Tuple[torch.Tensor, torch.Tensor],
+    _typing.Tuple[torch.Tensor, _typing.Iterable[torch.Tensor]]
+]:
+    """
+    Row-wise sorts :obj:`edge_index` and removes its duplicated entries.
+    Duplicate entries in :obj:`edge_attr` are directly removed, instead of merged.
+    Args:
+        edge_index (LongTensor): The edge indices.
+        edge_attr (Tensor or List[Tensor], optional): Edge weights or multi-
+            dimensional edge features.
+            If given as a list, will re-shuffle and remove duplicates for all
+            its entries. (default: :obj:`None`)
+        num_nodes (int, optional): The number of nodes, *i.e.*
+            :obj:`max_val + 1` of :attr:`edge_index`. (default: :obj:`None`)
+        is_sorted (bool, optional): If set to :obj:`True`, will expect
+            :obj:`edge_index` to be already sorted row-wise.
+        sort_by_row (bool, optional): If set to :obj:`False`, will sort
+            :obj:`edge_index` column-wise.
+    :rtype: :class:`LongTensor` if :attr:`edge_attr` is :obj:`None`, else
+        (:class:`LongTensor`, :obj:`Tensor` or :obj:`Iterable[Tensor]]`)
+    """
+
+    if edge_attr is None:
+        pass
+    elif isinstance(edge_attr, torch.Tensor) and torch.is_tensor(edge_attr):
+        if edge_attr.size(0) != edge_index.size(1):
+            raise ValueError
+    elif isinstance(edge_attr, _typing.Iterable):
+        if not all([
+            (
+                    isinstance(attr, torch.Tensor) and
+                    attr.size(0) == edge_index.size(1)
+            ) for attr in edge_attr
+        ]):
+            raise ValueError("Invalid edge_attr argument")
+    else:
+        raise TypeError("Unsupported type of edge_attr argument")
+
+    nnz = edge_index.size(1)
+    num_nodes = _maybe_num_nodes(edge_index, num_nodes)
+
+    idx = edge_index.new_empty(nnz + 1)
+    idx[0] = -1
+    idx[1:] = edge_index[1 - int(sort_by_row)]
+    idx[1:].mul_(num_nodes).add_(edge_index[int(sort_by_row)])
+
+    if not is_sorted:
+        idx[1:], perm = idx[1:].sort()
+        edge_index = edge_index[:, perm]
+        if edge_attr is not None and isinstance(edge_attr, torch.Tensor):
+            edge_attr = edge_attr[perm]
+        elif edge_attr is not None:
+            edge_attr = [e[perm] for e in edge_attr]
+
+    mask: _typing.Any = idx[1:] > idx[:-1]
+
+    # Only perform expensive merging in case there exists duplicates:
+    if mask.all():
+        return edge_index if edge_attr is None else (edge_index, edge_attr)
+
+    edge_index = edge_index[:, mask]
+    if edge_attr is None:
+        return edge_index
+    elif isinstance(edge_attr, torch.Tensor):
+        return edge_index, edge_attr[mask]
+    elif isinstance(edge_attr, _typing.Iterable):
+        return edge_index, [attr[mask] for attr in edge_attr]
+
+
+def coalesce(
+        edge_index: torch.Tensor,
+        edge_attr: _typing.Union[
+            torch.Tensor, _typing.Iterable[torch.Tensor], None
+        ] = None,
+        num_nodes: _typing.Optional[int] = ...,
+        is_sorted: bool = False,
+        sort_by_row: bool = True
+) -> _typing.Union[
+    torch.Tensor, _typing.Tuple[torch.Tensor, torch.Tensor],
+    _typing.Tuple[torch.Tensor, _typing.Iterable[torch.Tensor]]
+]:
+    """
+    Row-wise sorts :obj:`edge_index` and removes its duplicated entries.
+    Duplicate entries in :obj:`edge_attr` are directly removed, instead of merged.
+    Args:
+        edge_index (LongTensor): The edge indices.
+        edge_attr (Tensor or List[Tensor], optional): Edge weights or multi-
+            dimensional edge features.
+            If given as a list, will re-shuffle and remove duplicates for all
+            its entries. (default: :obj:`None`)
+        num_nodes (int, optional): The number of nodes, *i.e.*
+            :obj:`max_val + 1` of :attr:`edge_index`. (default: :obj:`None`)
+        is_sorted (bool, optional): If set to :obj:`True`, will expect
+            :obj:`edge_index` to be already sorted row-wise.
+        sort_by_row (bool, optional): If set to :obj:`False`, will sort
+            :obj:`edge_index` column-wise.
+    :rtype: :class:`LongTensor` if :attr:`edge_attr` is :obj:`None`, else
+        (:class:`LongTensor`, :obj:`Tensor` or :obj:`Iterable[Tensor]]`)
+    """
+    if not isinstance(num_nodes, int):
+        num_nodes = None
+    try:
+        import torch_geometric
+        return torch_geometric.utils.coalesce(
+            edge_index, edge_attr, num_nodes,
+            is_sorted=is_sorted,
+            sort_by_row=sort_by_row
+        )
+    except ModuleNotFoundError:
+        return __coalesce(
+            edge_index, edge_attr, num_nodes,
+            is_sorted=is_sorted,
+            sort_by_row=sort_by_row
+        )
+
+
+def to_undirected(
+        edge_index: torch.Tensor,
+        edge_attr: _typing.Optional[_typing.Union[torch.Tensor, _typing.List[torch.Tensor]]] = None,
+        num_nodes: _typing.Optional[int] = ...,
+        __reduce: str = "add",
+) -> _typing.Union[
+    torch.Tensor, _typing.Tuple[torch.Tensor, torch.Tensor],
+    _typing.Tuple[torch.Tensor, _typing.List[torch.Tensor]]
+]:
+    r"""Converts the graph given by :attr:`edge_index` to an undirected graph
+    such that :math:`(j,i) \in \mathcal{E}` for every edge :math:`(i,j) \in
+    \mathcal{E}`.
+
+    Args:
+        edge_index (LongTensor): The edge indices.
+        edge_attr (Tensor or List[Tensor], optional): Edge weights or multi-
+            dimensional edge features.
+            If given as a list, will remove duplicates for all its entries.
+            (default: :obj:`None`)
+        num_nodes (int, optional): The number of nodes, *i.e.*
+            :obj:`max_val + 1` of :attr:`edge_index`. (default: :obj:`None`)
+        __reduce (string, optional): The reduce operation to use for merging edge
+            features (:obj:`"add"`, :obj:`"mean"`, :obj:`"min"`, :obj:`"max"`,
+            :obj:`"mul"`). (default: :obj:`"add"`)
+
+    :rtype: :class:`LongTensor` if :attr:`edge_attr` is :obj:`None`, else
+        (:class:`LongTensor`, :obj:`Tensor` or :obj:`List[Tensor]]`)
+    """
+    # Maintain backward compatibility to `to_undirected(edge_index, num_nodes)`
+    if isinstance(edge_attr, int):
+        edge_attr = None
+        num_nodes = edge_attr
+
+    row, col = edge_index
+    row, col = torch.cat([row, col], dim=0), torch.cat([col, row], dim=0)
+    edge_index = torch.stack([row, col], dim=0)
+
+    if edge_attr is not None and isinstance(edge_attr, torch.Tensor):
+        edge_attr = torch.cat([edge_attr, edge_attr], dim=0)
+    elif edge_attr is not None:
+        edge_attr = [torch.cat([e, e], dim=0) for e in edge_attr]
+
+    return coalesce(edge_index, edge_attr, num_nodes)
+
+
+class _SplitResult:
+    def __init__(
+            self,
+            train_pos_edge_index: torch.Tensor,
+            train_pos_edge_attr: _typing.Optional[torch.Tensor],
+            train_neg_adj_mask: torch.Tensor,
+            val_pos_edge_index: torch.Tensor,
+            val_pos_edge_attr: _typing.Optional[torch.Tensor],
+            val_neg_edge_index: torch.Tensor,
+            test_pos_edge_index: torch.Tensor,
+            test_pos_edge_attr: _typing.Optional[torch.Tensor],
+            test_neg_edge_index: torch.Tensor
+    ):
+        self.train_pos_edge_index: torch.Tensor = train_pos_edge_index
+        self.train_pos_edge_attr: _typing.Optional[torch.Tensor] = train_pos_edge_attr
+        self.train_neg_adj_mask: torch.Tensor = train_neg_adj_mask
+        self.val_pos_edge_index: torch.Tensor = val_pos_edge_index
+        self.val_pos_edge_attr: _typing.Optional[torch.Tensor] = val_pos_edge_attr
+        self.val_neg_edge_index: torch.Tensor = val_neg_edge_index
+        self.test_pos_edge_index: torch.Tensor = test_pos_edge_index
+        self.test_pos_edge_attr: _typing.Optional[torch.Tensor] = test_pos_edge_attr
+        self.test_neg_edge_index: torch.Tensor = test_neg_edge_index
+
+
+def train_test_split_edges(
+        edge_index: torch.Tensor,
+        edge_attr: _typing.Optional[_typing.Union[torch.Tensor, _typing.List[torch.Tensor]]] = None,
+        num_nodes: _typing.Optional[int] = ...,
+        val_ratio: float = 0.05,
+        test_ratio: float = 0.1
+):
+    r"""Splits the edges of a :class:`torch_geometric.data.Data` object
+    into positive and negative train/val/test edges.
+    As such, it will replace the :obj:`edge_index` attribute with
+    :obj:`train_pos_edge_index`, :obj:`train_pos_neg_adj_mask`,
+    :obj:`val_pos_edge_index`, :obj:`val_neg_edge_index` and
+    :obj:`test_pos_edge_index` attributes.
+    If :obj:`data` has edge features named :obj:`edge_attr`, then
+    :obj:`train_pos_edge_attr`, :obj:`val_pos_edge_attr` and
+    :obj:`test_pos_edge_attr` will be added as well.
+
+    .. warning::
+
+        :meth:`~torch_geometric.utils.train_test_split_edges` is deprecated and
+        will be removed in a future release.
+        Use :class:`torch_geometric.transforms.RandomLinkSplit` instead.
+
+    Args:
+        edge_index (LongTensor): The edge indices.
+        edge_attr (Tensor or List[Tensor], optional): Edge weights or multi-
+            dimensional edge features.
+            If given as a list, will remove duplicates for all its entries.
+            (default: :obj:`None`)
+        num_nodes (int, optional): The number of nodes, *i.e.*
+            :obj:`max_val + 1` of :attr:`edge_index`. (default: :obj:`None`)
+        val_ratio (float, optional): The ratio of positive validation edges.
+            (default: :obj:`0.05`)
+        test_ratio (float, optional): The ratio of positive test edges.
+            (default: :obj:`0.1`)
+    """
+    row, col = edge_index
+    num_nodes = _maybe_num_nodes(edge_index, num_nodes)
+
+    # Return upper triangular portion.
+    mask = row < col
+    row, col = row[mask], col[mask]
+
+    if edge_attr is not None:
+        edge_attr = edge_attr[mask]
+
+    n_v = int(math.floor(val_ratio * row.size(0)))
+    n_t = int(math.floor(test_ratio * row.size(0)))
+
+    # Positive edges.
+    perm = torch.randperm(row.size(0))
+    row, col = row[perm], col[perm]
+    if edge_attr is not None:
+        edge_attr = edge_attr[perm]
+
+    r, c = row[:n_v], col[:n_v]
+    val_pos_edge_index = torch.stack([r, c], dim=0)
+    if edge_attr is not None:
+        val_pos_edge_attr = edge_attr[:n_v]
+    else:
+        val_pos_edge_attr = None
+
+    r, c = row[n_v:n_v + n_t], col[n_v:n_v + n_t]
+    test_pos_edge_index = torch.stack([r, c], dim=0)
+    if edge_attr is not None:
+        test_pos_edge_attr = edge_attr[n_v:n_v + n_t]
+    else:
+        test_pos_edge_attr = None
+
+    r, c = row[n_v + n_t:], col[n_v + n_t:]
+    train_pos_edge_index = torch.stack([r, c], dim=0)
+    if edge_attr is not None:
+        train_pos_edge_index, train_pos_edge_attr = to_undirected(
+            train_pos_edge_index, edge_attr[n_v + n_t:]
+        )
+    else:
+        train_pos_edge_index = to_undirected(train_pos_edge_index)
+        train_pos_edge_attr = None
+
+    # Negative edges.
+    neg_adj_mask = torch.ones(num_nodes, num_nodes, dtype=torch.uint8)
+    neg_adj_mask = neg_adj_mask.triu(diagonal=1).to(torch.bool)
+    neg_adj_mask[row, col] = 0
+
+    neg_row, neg_col = neg_adj_mask.nonzero(as_tuple=False).t()
+    perm = torch.randperm(neg_row.size(0))[:n_v + n_t]
+    neg_row, neg_col = neg_row[perm], neg_col[perm]
+
+    neg_adj_mask[neg_row, neg_col] = 0
+    train_neg_adj_mask = neg_adj_mask
+
+    row, col = neg_row[:n_v], neg_col[:n_v]
+    val_neg_edge_index = torch.stack([row, col], dim=0)
+
+    row, col = neg_row[n_v:n_v + n_t], neg_col[n_v:n_v + n_t]
+    test_neg_edge_index = torch.stack([row, col], dim=0)
+
+    return _SplitResult(
+        train_pos_edge_index, train_pos_edge_attr, train_neg_adj_mask,
+        val_pos_edge_index, val_pos_edge_attr, val_neg_edge_index,
+        test_pos_edge_index, test_pos_edge_attr, test_neg_edge_index
+    )