Combine Two Weighted Graphs In Networkx

I'm using python multiprocessing to create multiple different NetworkX graphs and then using the below function to combine the graphs. However, while this function works fine for s

Solution 1:

There are two places where memory will expand in your code:

1) making a copy of graph1 (perhaps you need to keep a copy though)

2) using graph2.edges() makes a list of all edges in memory, graph2.edges_iter() iterates over the edges without creating a new list

You can probably make it faster by handling the edge data differently too. You can get the data object while iterating over the edges and not have to perform as may dictionary lookups:

defcombined_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
    returnif __name__ == '__main__':
    import networkx as nx
    G = nx.Graph([('a','b', {'a': 1, 'b':2})])
    H = nx.Graph([('a','b', {'a': 3, 'c':4})])
    printlist(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))