wehub-resource-sync
146 lines
4.1 KiB
Python
146 lines
4.1 KiB
Python
""" Code adapted from https://github.com/kavehhassani/mvgrl """
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import dgl
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import networkx as nx
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import numpy as np
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import scipy.sparse as sp
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import torch as th
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from dgl.data import CiteseerGraphDataset, CoraGraphDataset, PubmedGraphDataset
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from dgl.nn import APPNPConv
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from scipy.linalg import fractional_matrix_power, inv
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from sklearn.preprocessing import MinMaxScaler
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def preprocess_features(features):
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"""Row-normalize feature matrix and convert to tuple representation"""
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rowsum = np.array(features.sum(1))
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r_inv = np.power(rowsum, -1).flatten()
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r_inv[np.isinf(r_inv)] = 0.0
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r_mat_inv = sp.diags(r_inv)
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features = r_mat_inv.dot(features)
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if isinstance(features, np.ndarray):
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return features
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else:
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return features.todense(), sparse_to_tuple(features)
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def sparse_to_tuple(sparse_mx):
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"""Convert sparse matrix to tuple representation."""
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def to_tuple(mx):
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if not sp.isspmatrix_coo(mx):
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mx = mx.tocoo()
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coords = np.vstack((mx.row, mx.col)).transpose()
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values = mx.data
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shape = mx.shape
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return coords, values, shape
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if isinstance(sparse_mx, list):
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for i in range(len(sparse_mx)):
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sparse_mx[i] = to_tuple(sparse_mx[i])
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else:
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sparse_mx = to_tuple(sparse_mx)
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return sparse_mx
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def compute_ppr(graph: nx.Graph, alpha=0.2, self_loop=True):
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a = nx.convert_matrix.to_numpy_array(graph)
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if self_loop:
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a = a + np.eye(a.shape[0]) # A^ = A + I_n
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d = np.diag(np.sum(a, 1)) # D^ = Sigma A^_ii
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dinv = fractional_matrix_power(d, -0.5) # D^(-1/2)
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at = np.matmul(np.matmul(dinv, a), dinv) # A~ = D^(-1/2) x A^ x D^(-1/2)
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return alpha * inv(
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(np.eye(a.shape[0]) - (1 - alpha) * at)
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) # a(I_n-(1-a)A~)^-1
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def process_dataset(name, epsilon):
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if name == "cora":
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dataset = CoraGraphDataset()
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elif name == "citeseer":
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dataset = CiteseerGraphDataset()
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graph = dataset[0]
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feat = graph.ndata.pop("feat")
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label = graph.ndata.pop("label")
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train_mask = graph.ndata.pop("train_mask")
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val_mask = graph.ndata.pop("val_mask")
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test_mask = graph.ndata.pop("test_mask")
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train_idx = th.nonzero(train_mask, as_tuple=False).squeeze()
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val_idx = th.nonzero(val_mask, as_tuple=False).squeeze()
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test_idx = th.nonzero(test_mask, as_tuple=False).squeeze()
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nx_g = dgl.to_networkx(graph)
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print("computing ppr")
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diff_adj = compute_ppr(nx_g, 0.2)
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print("computing end")
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if name == "citeseer":
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print("additional processing")
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feat = th.tensor(preprocess_features(feat.numpy())).float()
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diff_adj[diff_adj < epsilon] = 0
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scaler = MinMaxScaler()
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scaler.fit(diff_adj)
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diff_adj = scaler.transform(diff_adj)
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diff_edges = np.nonzero(diff_adj)
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diff_weight = diff_adj[diff_edges]
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diff_graph = dgl.graph(diff_edges)
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graph = graph.add_self_loop()
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return (
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graph,
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diff_graph,
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feat,
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label,
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train_idx,
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val_idx,
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test_idx,
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diff_weight,
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)
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def process_dataset_appnp(epsilon):
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k = 20
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alpha = 0.2
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dataset = PubmedGraphDataset()
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graph = dataset[0]
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feat = graph.ndata.pop("feat")
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label = graph.ndata.pop("label")
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train_mask = graph.ndata.pop("train_mask")
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val_mask = graph.ndata.pop("val_mask")
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test_mask = graph.ndata.pop("test_mask")
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train_idx = th.nonzero(train_mask, as_tuple=False).squeeze()
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val_idx = th.nonzero(val_mask, as_tuple=False).squeeze()
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test_idx = th.nonzero(test_mask, as_tuple=False).squeeze()
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appnp = APPNPConv(k, alpha)
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id = th.eye(graph.num_nodes()).float()
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diff_adj = appnp(graph.add_self_loop(), id).numpy()
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diff_adj[diff_adj < epsilon] = 0
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scaler = MinMaxScaler()
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scaler.fit(diff_adj)
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diff_adj = scaler.transform(diff_adj)
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diff_edges = np.nonzero(diff_adj)
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diff_weight = diff_adj[diff_edges]
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diff_graph = dgl.graph(diff_edges)
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return (
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graph,
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diff_graph,
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feat,
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label,
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train_idx,
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val_idx,
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test_idx,
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diff_weight,
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)
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