代码

import numpy as np
import dgl
import networkx as nx
def numpy_to_graph(A,type_graph='dgl',node_features=None):
    '''Convert numpy arrays to graph
    Parameters
    ----------
    A : mxm array
        Adjacency matrix
    type_graph : str
        'dgl' or 'nx'
    node_features : dict
        Optional, dictionary with key=feature name, value=list of size m
        Allows user to specify node features
    Returns
    -------
    Graph of 'type_graph' specification
    G = nx.from_numpy_array(A)
    if node_features != None:
        for n in G.nodes():
            for k,v in node_features.items():
                G.nodes[n][k] = v[n]
    if type_graph == 'nx':
        return G
    G = G.to_directed()
    if node_features != None:
        node_attrs = list(node_features.keys())
    else:
        node_attrs = []
    g = dgl.from_networkx(G, node_attrs=node_attrs, edge_attrs=['weight'])
    return g

示例

邻接矩阵被传递给函数。此外，其他特征(即特征向量、标签等)可以在 node_features 中传递

# mxm adjacency matrix
A = np.array([[0,0,0],
              [2,0,0],
              [5,1,0]])
# Each m row is a feature vector for node m
F = np.array([[1,0,1,4,4],
             [2,4,0,12,4],
             [5,1,-4,2,9]])
G = numpy_to_graph(A,type_graph='nx',node_features={'feat':F})