GraphRNN2/main_baselines/graphvae/graphvae_data.py

import networkx as nx
import numpy as np
import torch

class GraphAdjSampler(torch.utils.data.Dataset):
    def __init__(self, G_list, max_num_nodes, features='id'):
        self.max_num_nodes = max_num_nodes
        self.adj_all = []
        self.len_all = []
        self.feature_all = []

        for G in G_list:
            adj = nx.to_numpy_matrix(G)
            # the diagonal entries are 1 since they denote node probability
            self.adj_all.append(
                    np.asarray(adj) + np.identity(G.number_of_nodes()))
            self.len_all.append(G.number_of_nodes())
            if features == 'id':
                self.feature_all.append(np.identity(max_num_nodes))
            elif features == 'deg':
                degs = np.sum(np.array(adj), 1)
                degs = np.expand_dims(np.pad(degs, [0, max_num_nodes - G.number_of_nodes()], 0),
                                      axis=1)
                self.feature_all.append(degs)
            elif features == 'struct':
                degs = np.sum(np.array(adj), 1)
                degs = np.expand_dims(np.pad(degs, [0, max_num_nodes - G.number_of_nodes()],
                                             'constant'),
                                      axis=1)
                clusterings = np.array(list(nx.clustering(G).values()))
                clusterings = np.expand_dims(np.pad(clusterings, 
                                                    [0, max_num_nodes - G.number_of_nodes()],
                                                    'constant'),
                                             axis=1)
                self.feature_all.append(np.hstack([degs, clusterings]))

    def __len__(self):
        return len(self.adj_all)

    def __getitem__(self, idx):
        adj = self.adj_all[idx]
        num_nodes = adj.shape[0]
        adj_padded = np.zeros((self.max_num_nodes, self.max_num_nodes))
        adj_padded[:num_nodes, :num_nodes] = adj

        adj_decoded = np.zeros(self.max_num_nodes * (self.max_num_nodes + 1) // 2)
        node_idx = 0
        
        adj_vectorized = adj_padded[np.triu(np.ones((self.max_num_nodes,self.max_num_nodes)) ) == 1]
        # the following 2 lines recover the upper triangle of the adj matrix
        #recovered = np.zeros((self.max_num_nodes, self.max_num_nodes))
        #recovered[np.triu(np.ones((self.max_num_nodes, self.max_num_nodes)) ) == 1] = adj_vectorized
        #print(recovered)
        
        return {'adj':adj_padded,
                'adj_decoded':adj_vectorized, 
                'features':self.feature_all[idx].copy()}