feat: add link generation script

link_gen.py

@@ -0,0 +1,108 @@
+import dgl
+from dgl.data import AmazonCoBuyPhotoDataset
+import torch
+import pickle
+from copy import deepcopy
+import scipy.sparse as sp
+import numpy as np
+import os
+
+
+def mask_test_edges(adj_orig, val_frac, test_frac):
+
+    # Remove diagonal elements
+    adj = deepcopy(adj_orig)
+    # set diag as all zero
+    adj.setdiag(0)
+    adj.eliminate_zeros()
+    # Check that diag is zero:
+    # assert np.diag(adj.todense()).sum() == 0
+
+    adj_triu = sp.triu(adj, 1)
+    edges = sparse_to_tuple(adj_triu)[0]
+    num_test = int(np.floor(edges.shape[0] * test_frac))
+    num_val = int(np.floor(edges.shape[0] * val_frac))
+
+    all_edge_idx = list(range(edges.shape[0]))
+    np.random.shuffle(all_edge_idx)
+    val_edge_idx = all_edge_idx[:num_val]
+    test_edge_idx = all_edge_idx[num_val : (num_val + num_test)]
+    test_edges = edges[test_edge_idx]
+    val_edges = edges[val_edge_idx]
+    train_edges = edges[all_edge_idx[num_val + num_test :]]
+
+    noedge_mask = np.ones(adj.shape) - adj
+    noedges = np.asarray(sp.triu(noedge_mask, 1).nonzero()).T
+    all_edge_idx = list(range(noedges.shape[0]))
+    np.random.shuffle(all_edge_idx)
+    val_edge_idx = all_edge_idx[:num_val]
+    test_edge_idx = all_edge_idx[num_val : (num_val + num_test)]
+    test_edges_false = noedges[test_edge_idx]
+    val_edges_false = noedges[val_edge_idx]
+
+    data = np.ones(train_edges.shape[0])
+    adj_train = sp.csr_matrix(
+        (data, (train_edges[:, 0], train_edges[:, 1])), shape=adj.shape
+    )
+    adj_train = adj_train + adj_train.T
+
+    train_mask = np.ones(adj_train.shape)
+    for edges_tmp in [val_edges, val_edges_false, test_edges, test_edges_false]:
+        for e in edges_tmp:
+            assert e[0] < e[1]
+        train_mask[edges_tmp.T[0], edges_tmp.T[1]] = 0
+        train_mask[edges_tmp.T[1], edges_tmp.T[0]] = 0
+
+    train_edges = np.asarray(sp.triu(adj_train, 1).nonzero()).T
+    train_edges_false = np.asarray(
+        (sp.triu(train_mask, 1) - sp.triu(adj_train, 1)).nonzero()
+    ).T
+
+    # NOTE: all these edge lists only contain single direction of edge!
+    return (
+        train_edges,
+        train_edges_false,
+        val_edges,
+        val_edges_false,
+        test_edges,
+        test_edges_false,
+    )
+
+
+def sparse_to_tuple(sparse_mx):
+    if not sp.isspmatrix_coo(sparse_mx):
+        sparse_mx = sparse_mx.tocoo()
+    coords = np.vstack((sparse_mx.row, sparse_mx.col)).transpose()
+    values = sparse_mx.data
+    shape = sparse_mx.shape
+    return coords, values, shape
+
+
+if __name__ == "__main__":
+    os.mkdir("links")
+    os.mkdir("pretrain_labels")
+    g = AmazonCoBuyPhotoDataset()[0]
+    total_pos_edges = torch.randperm(g.num_edges())
+    adj_train = g.adjacency_matrix(scipy_fmt="csr")
+    (
+        train_edges,
+        train_edges_false,
+        val_edges,
+        val_edges_false,
+        test_edges,
+        test_edges_false,
+    ) = mask_test_edges(adj_train, 0.1, 0.2)
+    tvt_edges_file = "links/co_photo_tvtEdges.pkl"
+    pickle.dump(
+        (
+            train_edges,
+            train_edges_false,
+            val_edges,
+            val_edges_false,
+            test_edges,
+            test_edges_false,
+        ),
+        open(tvt_edges_file, "wb"),
+    )
+    node_assignment = dgl.metis_partition_assignment(g, 10)
+    torch.save(node_assignment, "pretrain_labels/metis_label_co_photo.pt")