Link_Predictors.py 文件源码

def 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 = 0.0
        A3 = 0.0
        for path in paths:
            if len(path) == 3:
                A2 = A2 + 1.0
            elif len(path) == 4:
                A3 = A3 + 1.0
        return  A2 + 0.001 * A3 #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)