examples/graph/cgscore_datasets.py

# compute cgscore for gcn
# author: Yaning
import torch
import numpy as np
import torch.nn.functional as Fd
from deeprobust.graph.defense import GCNJaccard, GCN
from deeprobust.graph.defense import GCNScore
from deeprobust.graph.utils import *
from deeprobust.graph.data import Dataset, PrePtbDataset
from scipy.sparse import csr_matrix
import argparse
import pickle
from deeprobust.graph import utils
from collections import defaultdict

parser = argparse.ArgumentParser()
parser.add_argument('--seed', type=int, default=15, help='Random seed.')
parser.add_argument('--dataset', type=str, default='polblogs', choices=['cora', 'cora_ml', 'citeseer', 'polblogs', 'pubmed'], help='dataset')
parser.add_argument('--ptb_rate', type=float, default=0.10,  help='pertubation rate')

args = parser.parse_args()
args.cuda = torch.cuda.is_available()
print('cuda: %s' % args.cuda)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# make sure you use the same data splits as you generated attacks
np.random.seed(args.seed)
if args.cuda:
    torch.cuda.manual_seed(args.seed)

# Here the random seed is to split the train/val/test data,
# we need to set the random seed to be the same as that when you generate the perturbed graph
# data = Dataset(root='/tmp/', name=args.dataset, setting='nettack', seed=15)
# Or we can just use setting='prognn' to get the splits
data = Dataset(root='/tmp/', name=args.dataset, setting='prognn')
adj, features, labels = data.adj, data.features, data.labels
idx_train, idx_val, idx_test = data.idx_train, data.idx_val, data.idx_test


perturbed_data = PrePtbDataset(root='/tmp/',
        name=args.dataset,
        attack_method='meta',
        ptb_rate=args.ptb_rate)

perturbed_adj = perturbed_data.adj
# perturbed_adj = adj

def save_cg_scores(cg_scores, filename="cg_scores.npy"):
    np.save(filename, cg_scores)
    print(f"CG-scores saved to {filename}")

def load_cg_scores_numpy(filename="cg_scores.npy"):
    cg_scores = np.load(filename, allow_pickle=True)
    print(f"CG-scores loaded from {filename}")
    return cg_scores

def calc_cg_score_gnn_with_sampling(
    A, X, labels, device, rep_num=1, unbalance_ratio=1, sub_term=False
):
    """
    Calculate CG-score for each edge in a graph with node labels and random sampling.

    Args:
        A: torch.Tensor
            Adjacency matrix of the graph (size: N x N).
        X: torch.Tensor
            Node features matrix (size: N x F).
        labels: torch.Tensor
            Node labels (size: N).
        device: torch.device
            Device to perform calculations.
        rep_num: int
            Number of repetitions for Monte Carlo sampling.
        unbalance_ratio: float
            Ratio of unbalanced data (1:unbalance_ratio).
        sub_term: bool
            If True, calculate and return sub-terms.

    Returns:
        cg_scores: dict
            Dictionary containing CG-scores for edges and optionally sub-terms.
    """
    N = A.shape[0]
    cg_scores = {
        "vi": np.zeros((N, N)),
        "ab": np.zeros((N, N)),
        "a2": np.zeros((N, N)),
        "b2": np.zeros((N, N)),
        "times": np.zeros((N, N)),
    }

    with torch.no_grad():
        for _ in range(rep_num):
            # Compute AX (node representations)
            AX = torch.matmul(A, X).to(device)
            norm_AX = AX / torch.norm(AX, dim=1, keepdim=True)

            # Group nodes by their labels
            dataset = defaultdict(list)
            data_idx = defaultdict(list)
            for i, label in enumerate(labels):
                dataset[label.item()].append(norm_AX[i].unsqueeze(0))  # Store normalized data
                data_idx[label.item()].append(i)  # Store indices

            # Convert to tensors
            for label, data_list in dataset.items():
                dataset[label] = torch.cat(data_list, dim=0)
                data_idx[label] = torch.tensor(data_idx[label], dtype=torch.long, device=device)

            # Calculate CG-scores for each label group
            for curr_label, curr_samples in dataset.items():
                curr_indices = data_idx[curr_label]
                curr_num = len(curr_samples)

                # Randomly sample a subset of current label examples
                chosen_curr_idx = np.random.choice(range(curr_num), curr_num, replace=False)
                chosen_curr_samples = curr_samples[chosen_curr_idx]
                chosen_curr_indices = curr_indices[chosen_curr_idx]

                # Sample negative examples from other classes
                neg_samples = torch.cat(
                    [dataset[l] for l in dataset if l != curr_label], dim=0
                )
                neg_indices = torch.cat(
                    [data_idx[l] for l in data_idx if l != curr_label], dim=0
                )
                neg_num = min(int(curr_num * unbalance_ratio), len(neg_samples))
                chosen_neg_samples = neg_samples[
                    torch.randperm(len(neg_samples))[:neg_num]
                ]

                # Combine positive and negative samples
                combined_samples = torch.cat([chosen_curr_samples, chosen_neg_samples], dim=0)
                y = torch.cat(
                    [torch.ones(len(chosen_curr_samples)), -torch.ones(neg_num)], dim=0
                ).to(device)

                # Compute the Gram matrix H^\infty
                H_inner = torch.matmul(combined_samples, combined_samples.T)
                del combined_samples
                ###
                H_inner = torch.clamp(H_inner, min=-1.0, max=1.0)
                ###
                H = H_inner * (np.pi - torch.acos(H_inner)) / (2 * np.pi)
                del H_inner

                H.fill_diagonal_(0.5)
                ##
                epsilon = 1e-6
                H = H + epsilon * torch.eye(H.size(0), device=H.device)
                ##
                invH = torch.inverse(H)
                del H
                original_error = y @ (invH @ y)

                # Compute CG-scores for each edge
                for i in chosen_curr_indices:
                    print("the node index:", i)
                    for j in range(i + 1, N):  # Upper triangular traversal
                        # print(j)
                        if A[i, j] == 0:  # Skip if no edge exists
                            continue

                        # Remove edge (i, j) to create A1
                        A1 = A.clone()
                        A1[i, j] = A1[j, i] = 0

                        # Recompute AX with A1
                        AX1 = torch.matmul(A1, X).to(device)
                        norm_AX1 = AX1 / torch.norm(AX1, dim=1, keepdim=True)

                        # Repeat error calculation with A1
                        curr_samples_A1 = norm_AX1[chosen_curr_indices]
                        neg_samples_A1 = norm_AX1[neg_indices]
                        chosen_neg_samples_A1 = neg_samples_A1[
                            torch.randperm(len(neg_samples_A1))[:neg_num]
                        ]
                        combined_samples_A1 = torch.cat(
                            [curr_samples_A1, chosen_neg_samples_A1], dim=0
                        )
                        H_inner_A1 = torch.matmul(combined_samples_A1, combined_samples_A1.T)

                        del combined_samples_A1
                        
                        ### trick1
                        H_inner_A1 = torch.clamp(H_inner_A1, min=-1.0, max=1.0)
                        ###

                        H_A1 = H_inner_A1 * (np.pi - torch.acos(H_inner_A1)) / (2 * np.pi)
                        del H_inner_A1
                        H_A1.fill_diagonal_(0.5)

                        ### trick2
                        epsilon = 1e-6
                        H_A1= H_A1 + epsilon * torch.eye(H_A1.size(0), device=H_A1.device)
                        ###
                        invH_A1 = torch.inverse(H_A1)
                        del H_A1

                        error_A1 = y @ (invH_A1 @ y)
                         
                        print("i:", i)
                        print("j:", j)
                        print("current score:", (original_error - error_A1).item())
                        # Compute the difference in error (CG-score)
                        cg_scores["vi"][i, j] += (original_error - error_A1).item()
                        cg_scores["vi"][j, i] = cg_scores["vi"][i, j]  # Symmetric
                        cg_scores["times"][i, j] += 1
                        cg_scores["times"][j, i] += 1

    # Normalize CG-scores by repetition count
    for key, values in cg_scores.items():
        if key == "times":
            continue
        cg_scores[key] = values / np.where(cg_scores["times"] > 0, cg_scores["times"], 1)

    return cg_scores if sub_term else cg_scores["vi"] 

def is_symmetric_sparse(adj): 
    """
    Check if a sparse matrix is symmetric.
    """
    # Check symmetry
    return (adj != adj.transpose()).nnz == 0  # .nnz is the number of non-zero elements

def make_symmetric_sparse(adj):
    """
    Ensure the sparse adjacency matrix is symmetrical.
    """
    # Make the matrix symmetric
    sym_adj = (adj + adj.transpose()) / 2
    return sym_adj

perturbed_adj = make_symmetric_sparse(perturbed_adj)

if type(perturbed_adj) is not torch.Tensor:
    features, perturbed_adj, labels = utils.to_tensor(features, perturbed_adj, labels)
else:
    features = features.to(device)
    perturbed_adj = perturbed_adj.to(device)
    labels = labels.to(device)

if utils.is_sparse_tensor(perturbed_adj):
    
    adj_norm = utils.normalize_adj_tensor(perturbed_adj, sparse=True)
else:
    adj_norm = utils.normalize_adj_tensor(perturbed_adj)

features = features.to_dense()
perturbed_adj = adj_norm.to_dense()


calc_cg_score =  calc_cg_score_gnn_with_sampling(perturbed_adj, features, labels, device, rep_num=1, unbalance_ratio=1, sub_term=False)
save_cg_scores(calc_cg_score, filename="cg_scores_polblogs_0.10.npy")
# print("completed"）