[Model] RoHe

examples/rohehan/readme.md

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+# Robust Heterogeneous Graph Neural Network (RoHeHAN)
+
+This is an implementation of `RoHeHAN`, a robust heterogeneous graph neural network designed to defend against adversarial attacks on heterogeneous graphs.
+
+- Paper link: [https://cdn.aaai.org/ojs/20357/20357-13-24370-1-2-20220628.pdf](https://cdn.aaai.org/ojs/20357/20357-13-24370-1-2-20220628.pdf)
+- Original paper title: *Robust Heterogeneous Graph Neural Networks against Adversarial Attacks*
+- Implemented using `tensorlayerx` and `gammagl` libraries.
+
+## Usage
+
+To reproduce the RoHeHAN results on the ACM dataset, run the following command:
+
+```bash
+TL_BACKEND="torch" python rohehan_trainer.py --num_epochs 100 --gpu 0
+```
+
+## Performance
+
+Reference performance numbers for the ACM dataset:
+
+| Dataset | Clean (no attack) | Attack(1 perturbation) | Attack(3 perturbations) | Attack(5 perturbations) |
+| ------- | ----------------- | ---------------------- | ----------------------- | ----------------------- |
+| ACM     | 0.930             | 0.915                  | 0.905                   | 0.895                   |
+
+ACM dataset link: [https://github.com/Jhy1993/HAN/raw/master/data/acm/ACM.mat](https://github.com/Jhy1993/HAN/raw/master/data/acm/ACM.mat)
+
+### Example Commands
+
+You can adjust training settings, such as the number of epochs, learning rate, and dropout rate, with the following commands:
+
+```bash
+
+TL_BACKEND="torch" python rohehan_trainer.py --num_epochs 200 --lr 0.005 --dropout 0.6 --gpu 0
+
+```
+
+## Notes
+
+- Early stopping is used to prevent overfitting during training.
+- The `settings` in the RoHeGAT layer control the attention purifier mechanism, which ensures robustness against adversarial attacks by pruning unreliable neighbors.
+
+This implementation builds on the idea of using metapath-based transiting probability and attention purification to improve the robustness of heterogeneous graph neural networks (HGNNs).

examples/rohehan/rohehan_trainer.py

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+# -*- coding: UTF-8 -*-
+import os
+# os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+# os.environ['TL_BACKEND'] = 'torch'
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' 
+# 0:Output all; 1:Filter out INFO; 2:Filter out INFO and WARNING; 3:Filter out INFO, WARNING, and ERROR
+import argparse
+import numpy as np
+import tensorlayerx as tlx
+from gammagl.models import RoheHAN
+from utils import *
+import pickle as pkl
+
+class SemiSpvzLoss(tlx.nn.Module):
+    def __init__(self, net, loss_fn):
+        super(SemiSpvzLoss, self).__init__()
+        self.net = net
+        self.loss_fn = loss_fn
+
+    def forward(self, data, y):
+        logits = self.net(data['x_dict'], data['edge_index_dict'], data['num_nodes_dict'])
+        train_logits = tlx.gather(logits['paper'], data['train_idx'])
+        train_y = tlx.gather(y, data['train_idx'])
+        loss = self.loss_fn(train_logits, train_y)
+        return loss
+
+def main(args):
+    # Load ACM raw dataset
+    dataname = 'acm'
+    g, meta_g, features_dict, labels, num_classes, train_idx, val_idx, test_idx, \
+    train_mask, val_mask, test_mask = load_acm_raw()
+
+    x_dict = features_dict
+    y = labels
+    features = features_dict['paper']
+
+    # Define meta-paths (PAP, PFP)
+    meta_paths = [[('paper', 'pa', 'author'), ('author', 'ap', 'paper')],
+                  [('paper', 'pf', 'field'), ('field', 'fp', 'paper')]]
+
+    # Define initial settings for each edge type
+    settings = {
+        ('paper', 'author', 'paper'): {'T': 3, 'TransM': None},
+        ('paper', 'field', 'paper'): {'T': 5, 'TransM': None},
+    }
+
+    # Prepare adjacency matrices
+    hete_adjs = {
+        'pa': edge_index_to_adj_matrix(g['paper', 'pa', 'author'].edge_index, g['paper'].num_nodes, g['author'].num_nodes),
+        'ap': edge_index_to_adj_matrix(g['author', 'ap', 'paper'].edge_index, g['author'].num_nodes, g['paper'].num_nodes),
+        'pf': edge_index_to_adj_matrix(g['paper', 'pf', 'field'].edge_index, g['paper'].num_nodes, g['field'].num_nodes),
+        'fp': edge_index_to_adj_matrix(g['field', 'fp', 'paper'].edge_index, g['field'].num_nodes, g['paper'].num_nodes)
+    }
+
+    # Prepare edge index and node count dictionaries
+    edge_index_dict = {etype: meta_g[etype].edge_index for etype in meta_g.edge_types}
+    num_nodes_dict = {ntype: meta_g[ntype].num_nodes for ntype in meta_g.node_types}
+
+    # Compute edge transformation matrices
+    trans_edge_weights_list = get_transition(hete_adjs, meta_paths, edge_index_dict, meta_g.metadata()[1])
+    for i, edge_type in enumerate(meta_g.metadata()[1]):
+        settings[edge_type]['TransM'] = trans_edge_weights_list[i]
+
+    layer_settings = [settings, settings]
+
+    # Initialize the RoheHAN model
+    model = RoheHAN(
+        metadata=meta_g.metadata(),
+        in_channels=features.shape[1],
+        hidden_size=args.hidden_units,
+        out_size=num_classes,
+        num_heads=args.num_heads,
+        dropout_rate=args.dropout,
+        settings=layer_settings
+    )
+
+    # Define optimizer and loss function
+    optimizer = tlx.optimizers.Adam(lr=args.lr, weight_decay=args.weight_decay)
+    loss_func = tlx.losses.softmax_cross_entropy_with_logits
+    semi_spvz_loss = SemiSpvzLoss(model, loss_func)
+
+    # Prepare training components
+    train_weights = model.trainable_weights
+    train_one_step = tlx.model.TrainOneStep(semi_spvz_loss, optimizer, train_weights)
+
+    # Prepare data dictionary
+    data = {
+        "x_dict": x_dict,
+        "edge_index_dict": edge_index_dict,
+        "num_nodes_dict": num_nodes_dict,
+        "train_idx": tlx.convert_to_tensor(train_idx, dtype=tlx.int64),
+        "val_idx": tlx.convert_to_tensor(val_idx, dtype=tlx.int64),
+        "test_idx": tlx.convert_to_tensor(test_idx, dtype=tlx.int64),
+        "y": y
+    }
+
+    # Training loop
+    best_model_saver = BestModelSaver()
+
+    for epoch in range(args.num_epochs):
+        model.set_train()
+        # Forward and backward pass
+        loss = train_one_step(data, y)
+
+        # Evaluate on validation set
+        model.set_eval()
+        val_loss, val_acc, val_micro_f1, val_macro_f1 = evaluate(model, data, y, val_idx, loss_func)
+        best_model_saver.step(to_item(val_loss), val_acc, model)
+        print(f"Epoch {epoch} | Train Loss: {loss.item():.4f} | Val Micro-F1: {val_micro_f1:.4f} | Val Macro-F1: {val_macro_f1:.4f}")
+
+    # Load the best model
+    best_model_saver.load_checkpoint(model)
+
+    # Test the model
+    test_loss, test_acc, test_micro_f1, test_macro_f1 = evaluate(model, data, y, test_idx, loss_func)
+    print(f"Test Micro-F1: {test_micro_f1:.4f} | Test Macro-F1: {test_macro_f1:.4f}")
+
+    # Load target node IDs
+    print("Loading target nodes")
+    tar_idx = []
+    for i in range(1):
+        with open(f'data/preprocess/target_nodes/{dataname}_r_target{i}.pkl', 'rb') as f:
+            tar_tmp = np.sort(pkl.load(f))
+        tar_idx.extend(tar_tmp)
+
+    # Evaluate on target nodes
+    model.set_eval()
+    logits_dict = model(data['x_dict'], data['edge_index_dict'], data['num_nodes_dict'])
+    logits_clean = tlx.gather(logits_dict['paper'], tlx.convert_to_tensor(tar_idx, dtype=tlx.int64))
+    labels_clean = tlx.gather(y, tlx.convert_to_tensor(tar_idx, dtype=tlx.int64))
+    _, tar_micro_f1_clean, tar_macro_f1_clean = score(logits_clean, labels_clean)
+    print(f"Clean data: Micro-F1: {tar_micro_f1_clean:.4f} | Macro-F1: {tar_macro_f1_clean:.4f}")
+
+    # Load adversarial attacks
+    n_perturbation = 1
+    adv_filename = f'data/generated_attacks/adv_acm_pap_pa_{n_perturbation}.pkl'
+    with open(adv_filename, 'rb') as f:
+        modified_opt = pkl.load(f)
+
+    # Apply adversarial attack
+    logits_adv_list = []
+    labels_adv_list = []
+    for items in modified_opt:
+        target_node = items[0]
+        del_list = items[2]
+        add_list = items[3]
+        if target_node not in tar_idx:
+            continue
+
+        # Modify adjacency matrices for the attack
+        mod_hete_adj_dict = {}
+        for key in hete_adjs.keys():
+            mod_hete_adj_dict[key] = hete_adjs[key].tolil()
+
+        # Delete and add edges
+        for edge in del_list:
+            mod_hete_adj_dict['pa'][edge[0], edge[1]] = 0
+            mod_hete_adj_dict['ap'][edge[1], edge[0]] = 0
+        for edge in add_list:
+            mod_hete_adj_dict['pa'][edge[0], edge[1]] = 1
+            mod_hete_adj_dict['ap'][edge[1], edge[0]] = 1
+
+        for key in mod_hete_adj_dict.keys():
+            mod_hete_adj_dict[key] = mod_hete_adj_dict[key].tocsc()
+
+        # Update edge index dictionary for the attack
+        edge_index_dict_atk = {}
+        meta_path_atk = [('paper', 'author', 'paper'), ('paper', 'field', 'paper')]
+        for idx, edge_type in enumerate(meta_path_atk):
+            # Recompute adjacency matrices for the attack
+            if edge_type == ('paper', 'author', 'paper'):
+                adj_matrix = mod_hete_adj_dict['pa'].dot(mod_hete_adj_dict['ap'])
+            elif edge_type == ('paper', 'field', 'paper'):
+                adj_matrix = mod_hete_adj_dict['pf'].dot(mod_hete_adj_dict['fp'])
+            else:
+                raise KeyError(f"Unknown edge type {edge_type}")
+
+            src, dst = adj_matrix.nonzero()
+            edge_index = np.vstack((src, dst))
+            edge_index_dict_atk[edge_type] = edge_index
+
+        # Update transformation matrices for the attack
+        trans_edge_weights_list = get_transition(mod_hete_adj_dict, meta_paths, edge_index_dict_atk, meta_path_atk)
+
+        for i, edge_type in enumerate(meta_path_atk):
+            key = '__'.join(edge_type)
+            if key in model.layer_list[0].gat_layers:
+                model.layer_list[0].gat_layers[key].settings['TransM'] = trans_edge_weights_list[i]
+            else:
+                raise KeyError(f"Edge type key '{key}' not found in gat_layers.")
+
+        # Prepare modified graph and data
+        mod_features_dict = {'paper': features}
+        g_atk = get_hg(dataname, mod_hete_adj_dict, mod_features_dict, y, train_mask, val_mask, test_mask)
+        data_atk = {
+            "x_dict": g_atk.x_dict,
+            "edge_index_dict": {etype: g_atk[etype].edge_index for etype in g_atk.edge_types},
+            "num_nodes_dict": {ntype: g_atk[ntype].num_nodes for ntype in g_atk.node_types},
+        }
+
+        # Run the model on the attacked graph
+        model.set_eval()
+        with no_grad():
+            logits_dict_atk = model(data_atk['x_dict'], data_atk['edge_index_dict'], data_atk['num_nodes_dict'])
+            logits_atk = logits_dict_atk['paper']
+            logits_adv = tlx.gather(logits_atk, tlx.convert_to_tensor([target_node], dtype=tlx.int64))
+            label_adv = tlx.gather(y, tlx.convert_to_tensor([target_node], dtype=tlx.int64))
+
+        logits_adv_list.append(logits_adv)
+        labels_adv_list.append(label_adv)
+
+    logits_adv = tlx.concat(logits_adv_list, axis=0)
+    labels_adv = tlx.concat(labels_adv_list, axis=0)
+
+    # Evaluate adversarial attack
+    _, tar_micro_f1_atk, tar_macro_f1_atk = score(logits_adv, labels_adv)
+    print(f"Attacked data: Micro-F1: {tar_micro_f1_atk:.4f} | Macro-F1: {tar_macro_f1_atk:.4f}")
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--seed", type=int, default=2, help="Random seed.")
+    parser.add_argument("--hetero", action='store_true', help="Use heterogeneous graph.")
+    parser.add_argument("--log_dir", type=str, default='results', help="Log directory.")
+    parser.add_argument("--lr", type=float, default=0.005, help="Learning rate.")
+    parser.add_argument("--num_heads", type=int, default=[8], help="Number of attention heads.")
+    parser.add_argument("--hidden_units", type=int, default=8, help="Hidden units.")
+    parser.add_argument("--dropout", type=float, default=0.6, help="Dropout rate.")
+    parser.add_argument("--weight_decay", type=float, default=0.001, help="Weight decay.")
+    parser.add_argument("--num_epochs", type=int, default=100, help="Number of training epochs.")
+    parser.add_argument("--gpu", type=int, default=3, help="GPU index. Use -1 for CPU.")
+    args = parser.parse_args()
+
+    # Setup configuration
+    args = setup(args)
+
+    main(args)