[Model] GCIL (#226)

* 更新gcil模型与训练

* 更新

* 更新

* 删除多余文件

* fix bugs

---------

Co-authored-by: jxy <865526875@qq.com>

examples/gcil/README.md

@@ -0,0 +1,35 @@
+# Graph Contrastive Invariant Learning from the Causal Perspective (GCIL)
+
+- Paper link: [https://arxiv.org/pdf/2401.12564v2](https://arxiv.org/pdf/2401.12564v2)
+- Author's code repo: [https://github.com/BUPT-GAMMA/GCIL](https://github.com/BUPT-GAMMA/GCIL). Note that the original code is 
+  implemented with Tensorflow for the paper. 
+
+# Dataset Statics
+
+| Dataset | # Nodes | # Edges | # Classes |
+| ------- | ------- | ------- | --------- |
+| Cora    | 2,708   | 10,556  | 7         |
+| Pubmed  | 19,717  | 88,651  | 3         |
+
+Refer to [Planetoid](https://gammagl.readthedocs.io/en/latest/api/gammagl.datasets.html#gammagl.datasets.Planetoid).
+
+Results
+-------
+
+```bash
+# available dataset: "cora", "pubmed"
+TL_BACKEND="torch" python gcil_trainer.py cora
+TL_BACKEND="torch" python gcil_trainer.py pubmed 
+```
+
+Ma-F1:
+| Dataset | Paper    | Our(th)    |
+| ------- | -------- | ---------- |
+| cora    | 83.8±0.5 | 45.19±0.22 |
+| pubmed  | 81.5±0.5 | 46.30±0.02 |
+
+Mi-F1
+| Dataset | Paper    | Our(th)    |
+| ------- | -------- | ---------- |
+| cora    | 84.4±0.7 | 49.71±0.22 |
+| pubmed  | 81.6±0.7 | 53.77±0.01 |

examples/gcil/aug.py

@@ -0,0 +1,50 @@
+import torch as th
+import numpy as np
+from gammagl.data import Graph
+
+
+def random_aug(graph, attr, diag_attr, x, feat_drop_rate, edge_mask_rate, device):
+    n_node = graph.num_nodes if hasattr(graph, 'num_nodes') else graph.x.shape[0]
+
+    edge_mask = mask_edge(graph, edge_mask_rate)
+
+    feat = drop_feature(x, feat_drop_rate)
+
+    edge_index = graph.edge_index[:, edge_mask].long()  
+
+    edge_weight = attr[edge_mask] if attr is not None else None
+
+    if isinstance(attr, np.ndarray):
+        attr_cpu = attr.cpu().numpy() if attr.is_cuda else attr.numpy()
+        diag_attr_cpu = diag_attr.cpu().numpy() if diag_attr.is_cuda else diag_attr.numpy()
+        attr = np.concatenate([attr_cpu[edge_mask], diag_attr_cpu], axis=0)
+
+    new_graph = Graph(x=feat, edge_index=edge_index)
+
+    new_graph.x = new_graph.x.to(device)
+    new_graph.edge_index = new_graph.edge_index.to(device)
+
+    if edge_weight is not None:
+        edge_weight = th.tensor(edge_weight, dtype=th.float32).to(device)  # 确保 edge_weight 是 FloatTensor
+
+    return new_graph, edge_weight, feat
+
+
+
+def drop_feature(x, drop_prob):
+    drop_mask = th.empty(
+        (x.size(1),),
+        dtype=th.float32,
+        device=x.device).uniform_(0, 1) < drop_prob
+    x = x.clone()
+    x[:, drop_mask] = 0
+
+    return x
+
+
+def mask_edge(graph, edge_mask_rate):
+    E = graph.edge_index.shape[1]
+
+    mask = np.random.rand(E) > edge_mask_rate
+
+    return mask