Link_Predictors.py 文件源码

def weighted_local_path_index(G, ebunch=None):
    if ebunch is None:
        ebunch = nx.non_edges(G)

    def predict(u, v):
        #NeighborSet = nx.all_neighbors(G, u)
        #len( sorted(nx.common_neighbors(G, u, v) ))
        paths = list( nx.all_simple_paths(G, source=u, target=v, cutoff=3))
        #print paths
        A2_weight = 0.0
        A3_weight = 0.0
        for path in paths:
            if len(path) == 3:
                for node in range(0, len(path)-1):
                    A2_weight = A2_weight + G[path[node]][path[node+1]]['weight']
            elif len(path) == 4:
                for node in range(0, len(path)-1):
                    A3_weight = A3_weight + G[path[node]][path[node+1]]['weight']

        #value = sum(   G[w][u]['weight'] + G[w][v]['weight']  for w in nx.common_neighbors(G, u, v) )
        return  A2_weight + 0.001 * A3_weight #+value  #Coefficient = 0.001

    Rank_List = []
    for u, v in ebunch:
        Rank_List.append((u, v, predict(u, v)))
    return Rank_List #((u, v, predict(u, v)) for u, v in ebunch)
    #return ((u, v, predict(u, v)) for u, v in ebunch)