PyTorch-GNN/build_graph.py at master · ciphoo/PyTorch-GNN · GitHub

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# -*- coding: utf-8 -*-

"""
@Date: 2019/1/11

@Author: dreamhome

@Summary:  DGL graph.
"""
import dgl
import torch
import networkx as nx

import matplotlib.pyplot as plt


def build_karate_club_graph():
    g = dgl.DGLGraph()
    # add 34 nodes into the graph; nodes are labeled from 0~33
    g.add_nodes(34)
    # all 78 edges as a list of tuples
    edge_list = [(1, 0), (2, 0), (2, 1), (3, 0), (3, 1), (3, 2),
                 (4, 0), (5, 0), (6, 0), (6, 4), (6, 5), (7, 0), (7, 1),
                 (7, 2), (7, 3), (8, 0), (8, 2), (9, 2), (10, 0), (10, 4),
                 (10, 5), (11, 0), (12, 0), (12, 3), (13, 0), (13, 1), (13, 2),
                 (13, 3), (16, 5), (16, 6), (17, 0), (17, 1), (19, 0), (19, 1),
                 (21, 0), (21, 1), (25, 23), (25, 24), (27, 2), (27, 23),
                 (27, 24), (28, 2), (29, 23), (29, 26), (30, 1), (30, 8),
                 (31, 0), (31, 24), (31, 25), (31, 28), (32, 2), (32, 8),
                 (32, 14), (32, 15), (32, 18), (32, 20), (32, 22), (32, 23),
                 (32, 29), (32, 30), (32, 31), (33, 8), (33, 9), (33, 13),
                 (33, 14), (33, 15), (33, 18), (33, 19), (33, 20), (33, 22),
                 (33, 23), (33, 26), (33, 27), (33, 28), (33, 29), (33, 30),
                 (33, 31), (33, 32)]
    # add edges two lists of nodes: src and dst
    src, dst = tuple(zip(*edge_list))
    g.add_edges(src, dst)
    # edges are directional in DGL; make them bi-directional
    g.add_edges(dst, src)

    return g


if __name__ == '__main__':
    G = build_karate_club_graph()
    print('%d nodes.' % G.number_of_nodes())
    print('%d edges.' % G.number_of_edges())

    fig, ax = plt.subplots()
    fig.set_tight_layout(False)
    nx_G = G.to_networkx().to_undirected()
    pos = nx.kamada_kawai_layout(nx_G)
    nx.draw(nx_G, pos, with_labels=True, node_color=[[0.5, 0.5, 0.5]])
    plt.show()

    # assign features to nodes or edges
    G.ndata['feat'] = torch.eye(34)
    print(G.nodes[2].data['feat'])
    print(G.nodes[1, 2].data['feat'])