utils.py 文件源码

def compute_nystrom(ds_name, use_node_labels, embedding_dim, community_detection_method, kernels):
    if ds_name=="SYNTHETIC":
        graphs, labels = generate_synthetic()
    else:
        graphs, labels = load_data(ds_name, use_node_labels)
    communities, subgraphs = compute_communities(graphs, use_node_labels, community_detection_method)

    print("Number of communities: ", len(communities))
    lens = []
    for community in communities:
        lens.append(community.number_of_nodes())

    print("Average size: %.2f" % np.mean(lens))
    Q=[]
    for idx, k in enumerate(kernels):
        model = Nystrom(k, n_components=embedding_dim)
        model.fit(communities)
        Q_t = model.transform(communities)
        Q_t = np.vstack([np.zeros(embedding_dim), Q_t])
        Q.append(Q_t)

    return Q, subgraphs, labels, Q_t.shape