有效地遍历所有可能的权重节点图，并计算最大团大小>k的概率

def bron_kerbosch(graph, R, P, X, cliques):
    if not P and not X:
        cliques.append(R)
    for v in list(P):
        bron_kerbosch(graph, R.union([v]), P.intersection(graph[v]), X.intersection(graph[v]), cliques)
        P.remove(v)
        X.add(v)

def find_all_cliques(graph):
    cliques = []
    bron_kerbosch(graph, set(), set(graph.keys()), set(), cliques)
    return cliques

import multiprocessing as mp

def process_graph(graph):
    cliques = find_all_cliques(graph)
    max_clique_size = max(len(clique) for clique in cliques)
    return max_clique_size > k

def parallel_processing(graphs):
    pool = mp.Pool(mp.cpu_count())
    results = pool.map(process_graph, graphs)
    pool.close()
    pool.join()
    return sum(results) / len(results)

import random

def generate_random_graph(nodes, edge_probability):
    graph = {i: set() for i in range(nodes)}
    for i in range(nodes):
        for j in range(i+1, nodes):
            if random.random() < edge_probability:
                graph[i].add(j)
                graph[j].add(i)
    return graph

graphs = [generate_random_graph(10, 0.5) for _ in range(100)]
probability = parallel_processing(graphs)
print(f"Probability of max clique size > 3: {probability}")