在 NetworkX 中合并两个加权图

我使用 python 多重处理来创建多个不同的 NetworkX 图，然后使用下面的函数来组合这些图。然而，虽然这个函数对于小图工作得很好，但对于较大的图，它会使用大量内存，并且会挂在我的系统和内存密集型 AWS 系统上（仅使用系统中总内存的大约三分之一）。有没有更有效的方法来执行以下功能？

def combine_graphs(graph1, graph2, graph2_weight = 1):
    '''
    Given two graphs of different edge (but same node) structure (and the same type),
    combine the two graphs, summing all edge attributes and multiplying the second one's
    attributes by the desired weights. 

    E.g. if graph1.edge[a][b] = {'a': 1, 'b':2} and 
    graph2.edge[a][b] = {'a': 3, 'c': 4}, 
    with a weight of 1 the final graph edge should be 
    final_graph.edge[a][b] = {'a': 4, 'b': 2, 'c': 4} and with a weight 
    of .5 the final graph edge should be {'a': 2.5, 'b': 2, 'c': 2}.

    Inputs: Two graphs to be combined and a weight to give to the second graph
    '''

    if type(graph1) != type(graph2) or len(set(graph2.nodes()) - set(graph1.nodes())) > 0:
        raise Exception('Graphs must have the same type and graph 2 cannot have nodes that graph 1 does not have.')

    # make a copy of the new graph to ensure that it doesn't change
    new_graph = graph1.copy()

    # iterate over graph2's edges, adding them to graph1
    for node1, node2 in graph2.edges():
        # if that edge already exists, now iterate over the attributes
        if new_graph.has_edge(node1, node2):
            for attr in graph2.edge[node1][node2]:
                # if that attribute exists, sum the values, otherwise, simply copy attrs
                if new_graph.edge[node1][node2].get(attr) is not None:
                    # try adding weighted value: if it fails, it's probably not numeric so add the full value (the only other option is a list)
                    try:
                        new_graph.edge[node1][node2][attr] += graph2.edge[node1][node2][attr] * graph2_weight
                    except:
                        new_graph.edge[node1][node2][attr] += graph2.edge[node1][node2][attr]
                else:
                    try:
                        new_graph.edge[node1][node2][attr] = graph2.edge[node1][node2][attr] * graph2_weight
                    except:
                        new_graph.edge[node1][node2][attr] = graph2.edge[node1][node2][attr]

        # otherwise, add the new edge with all its atributes -- first, iterate through those attributes to weight them
        else:
            attr_dict = graph2.edge[node1][node2]
            for item in attr_dict:
                try:
                    attr_dict[item] = attr_dict[item] * graph2_weight
                except:
                    continue
            new_graph.add_edge(node1, node2, attr_dict = attr_dict)

    return new_graph

代码中有两个地方会扩展内存：

1）复制graph1（也许你需要保留一份副本）

2）使用graph2.edges()创建内存中所有边的列表，graph2.edges_iter()迭代边而不创建新列表

您也可以通过以不同方式处理边缘数据来使其更快。您可以在迭代边缘时获取数据对象，而不必执行字典查找：

def combined_graphs_edges(G, H, weight = 1.0):
    for u,v,hdata in H.edges_iter(data=True):
        # multply attributes of H by weight
        attr = dict( (key, value*weight) for key,value in hdata.items())
        # get data from G or use empty dict if no edge in G
        gdata = G[u].get(v,{})
        # add data from g
        # sum shared items
        shared = set(gdata) & set(hdata)
        attr.update(dict((key, attr[key] + gdata[key]) for key in shared))
        # non shared items
        non_shared = set(gdata) - set(hdata)
        attr.update(dict((key, gdata[key]) for key in non_shared))
        yield u,v,attr
    return


if __name__ == '__main__':
    import networkx as nx
    G = nx.Graph([('a','b', {'a': 1, 'b':2})])
    H = nx.Graph([('a','b', {'a': 3, 'c':4})])
    print list(combined_graphs_edges(G,H,weight=0.5))
    # or to make a new graph 
    graph = G.copy()
    graph.add_edges_from(combined_graphs_edges(G,H,weight=0.5))