add code to examples folder

examples/README.md

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+#Fingerprinting Graph Neural Networks
+
+Steps
+
+1. Create virtual env. Activate it.
+2. Install requirements
+   ```bash
+   pip install -r requirements.txt
+   ```
+3. Create folders
+
+   ```bash
+   mkdir -p  data models fingerprints plots
+   ```
+
+### GNN task types: Node Classification, Link Prediction, Graph Classification
+
+For Node Classification (NC): \
+  Folder name: node_class/ \
+  Filename Suffix: \*\_nc.\*
+
+For Link Prediction (LP): \
+  Folder name: link_pred/ \
+  Filename Suffix: \*\_lp.\*
+
+For Graph Classification (GC): \
+  Folder name: graph_class/ \
+  Filename Suffix: \*\_gc.\*
+
+ Example: \
+  `bash 
+    python node_class/train_nc.py ` \
+  `bash 
+    python link_pred/train_lp.py ` \
+  `bash 
+    python graph_class/train_gc.py `
+
+### For node classification task on Cora dataset (GCN arch)
+
+```bash
+python node_class/train_nc.py
+```
+
+```bash
+python node_class/fine_tune_pirate_nc.py
+```
+
+```bash
+python node_class/distill_students_nc.py
+```
+
+```bash
+python node_class/train_unrelated_nc.py
+```
+
+```bash
+python node_class/fingerprint_generator_nc.py
+```
+
+```bash
+python node_class/generate_univerifier_dataset_nc.py
+```
+
+```bash
+python train_univerifier.py --dataset fingerprints/univerifier_dataset_nc.pt --fingerprints_path fingerprints/fingerprints_nc.pt --out fingerprints/univerifier_nc.pt
+```
+
+```bash
+python node_class/eval_verifier_nc.py
+```
+
+Follow similar approach as Node Classification for Link Prediction on Citeseer dataset (GCN arch) and Graph Classification on ENZYMES dataset (Graphsage arch).
+
+Change argument paths for LP and GC for training univerifier
+
+```bash
+python train_univerifier.py --dataset fingerprints/univerifier_dataset_lp.pt --fingerprints_path fingerprints/fingerprints_lp.pt --out fingerprints/univerifier_lp.pt
+```
+
+```bash
+python train_univerifier.py --dataset fingerprints/univerifier_dataset_gc.pt --fingerprints_path fingerprints/fingerprints_gc.pt --out fingerprints/univerifier_gc.pt
+```
+
+To evaluate suspect GNNs for NC tasks
+ ```bash
+ python node_class/make_suspect_neg_nc.py
+ ```
+ ```bash
+ python node_class/score_suspect_nc.py  --suspect_pt models/suspects/neg_nc_seed9999.pt  --suspect_meta models/suspects/neg_nc_seed9999.json
+ ```
+

examples/graph_class/distill_students_gc.py

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+# Positive (pirated) models for GRAPH CLASSIFICATION on ENZYMES via DISTILLATION.
+# Teacher: trained GC model loaded from target_model_gc.pt
+# Students: GraphSAGE via get_model
+
+import argparse, json, random, torch
+from pathlib import Path
+
+import torch.nn.functional as F
+from torch_geometric.datasets import TUDataset
+from torch_geometric.loader import DataLoader
+from torch_geometric.transforms import NormalizeFeatures
+
+from gsage_gc import get_model
+
+
+def set_seed(s: int):
+    random.seed(s); torch.manual_seed(s); torch.cuda.manual_seed_all(s)
+
+
+def kd_loss(student_logits, teacher_logits):
+    return F.mse_loss(student_logits, teacher_logits)
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument('--meta_path', default='models/target_meta_gc.json')
+    ap.add_argument('--target_path', default='models/target_model_gc.pt')
+    ap.add_argument('--archs', default='gsage')
+    ap.add_argument('--epochs', type=int, default=10)
+    ap.add_argument('--lr', type=float, default=0.01)
+    ap.add_argument('--wd', type=float, default=5e-4)
+    ap.add_argument('--seed', type=int, default=0)
+    ap.add_argument('--count_per_arch', type=int, default=100)
+    ap.add_argument('--out_dir', type=str, default='models/positives')
+    ap.add_argument('--batch_size', type=int, default=64)
+    ap.add_argument('--student_hidden', type=int, default=64)
+    ap.add_argument('--student_layers', type=int, default=3)
+    ap.add_argument('--student_dropout', type=float, default=0.5)
+    args = ap.parse_args()
+
+    set_seed(args.seed)
+    Path(args.out_dir).mkdir(parents=True, exist_ok=True)
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+    dataset = TUDataset(root='data/ENZYMES', name='ENZYMES',
+                        use_node_attr=True, transform=NormalizeFeatures())
+    in_dim = dataset.num_features
+    num_classes = dataset.num_classes
+    loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True)
+
+    # Teacher GC model
+    with open(args.meta_path, 'r') as f:
+        meta = json.load(f)
+    arch_t   = meta.get('arch', 'gsage')
+    hidden_t = meta.get('hidden', 64)
+    layers_t = meta.get('layers', 3)
+    drop_t   = meta.get('dropout', 0.5)
+
+    teacher = get_model(arch_t, in_dim, hidden_t, num_classes,
+                        num_layers=layers_t, dropout=drop_t, pool="mean").to(device)
+    teacher.load_state_dict(torch.load(args.target_path, map_location='cpu'))
+    teacher.eval()
+
+    archs = [a.strip() for a in args.archs.split(',') if a.strip()]
+    saved = []
+
+    for arch in archs:
+        for i in range(args.count_per_arch):
+            # fresh student
+            student = get_model(arch, in_dim, args.student_hidden, num_classes,
+                                num_layers=args.student_layers,
+                                dropout=args.student_dropout, pool="mean").to(device)
+            opt = torch.optim.Adam(student.parameters(), lr=args.lr, weight_decay=args.wd)
+
+            for _ in range(args.epochs):
+                student.train()
+                for batch in loader:
+                    batch = batch.to(device)
+                    with torch.no_grad():
+                        t_logits = teacher(batch.x, batch.edge_index, batch=batch.batch)  # [B, C]
+                    s_logits = student(batch.x, batch.edge_index, batch=batch.batch)      # [B, C]
+                    loss = kd_loss(s_logits, t_logits)
+                    opt.zero_grad(); loss.backward(); opt.step()
+
+            # save student
+            out_pt = f'{args.out_dir}/distill_gc_{arch}_{i:03d}.pt'
+            torch.save(student.state_dict(), out_pt)
+            with open(out_pt.replace('.pt', '.json'), 'w') as f:
+                json.dump({
+                    "task": "graph_classification",
+                    "dataset": "ENZYMES",
+                    "arch": arch,
+                    "hidden": args.student_hidden,
+                    "layers": args.student_layers,
+                    "dropout": args.student_dropout,
+                    "pos_kind": "distill",
+                    "teacher_arch": arch_t,
+                    "teacher_hidden": hidden_t,
+                    "teacher_layers": layers_t,
+                    "teacher_dropout": drop_t
+                }, f, indent=2)
+
+            saved.append(out_pt)
+            print(f"[distill-gc] saved {out_pt}")
+
+    print(f"Saved {len(saved)} distilled GC positives.")
+
+
+if __name__ == '__main__':
+    main()