networkx中使用modularity算法代码

可以使用以下代码实现networkx中的modularity算法：

import networkx as nx
from networkx.algorithms.community.modularity_max import greedy_modularity_communities

# 创建一个图对象
G = nx.Graph()

# 添加节点和边
G.add_edges_from([(1, 2), (1, 3), (2, 3), (3, 4), (4, 5), (4, 6), (5, 6)])

# 使用modularity算法获取社区
communities = greedy_modularity_communities(G)

# 打印社区
print(communities)

这段代码会输出图G中的社区。

相关问题

networkx中的派系过滤算法代码

以下是 networkx 中的派系过滤算法代码：

import networkx as nx
from networkx.algorithms.community import greedy_modularity_communities

G = nx.karate_club_graph()

# 使用贪心算法获取社区
communities = greedy_modularity_communities(G)

# 输出社区
for i, c in enumerate(communities):
    print(f"Community {i+1}: {c}")

希望能对您有所帮助。

在networkx中实现BGLL算法的代码

以下是实现BGLL算法的代码：

import networkx as nx
from networkx.algorithms.community import greedy_modularity_communities

def BGLL_algorithm(G):
    # Step 1: Initialize
    communities = list(greedy_modularity_communities(G))
    num_communities = len(communities)
    q = nx.algorithms.community.modularity(G, communities)

    # Step 2: Repeat until no improvement
    improvement = True
    while improvement:
        improvement = False
        for i in range(num_communities):
            for j in range(i+1, num_communities):
                # Step 3: Compute delta Q
                delta_q = compute_delta_q(G, communities, i, j)

                # Step 4: If delta Q is positive, merge the communities
                if delta_q > 0:
                    merge_communities(communities, i, j)
                    num_communities -= 1
                    q += delta_q
                    improvement = True

    return list(communities)

def compute_delta_q(G, communities, i, j):
    # Compute the change in modularity if communities i and j are merged
    delta_q = 0
    for node in communities[i]:
        for neighbor in G.neighbors(node):
            if neighbor in communities[j]:
                delta_q += 2 * (G[node][neighbor]['weight'] - compute_expected_weight(G, communities[i], communities[j]))
    return delta_q

def compute_expected_weight(G, community1, community2):
    # Compute the expected weight between community1 and community2
    total_weight = sum(G.degree(node, weight='weight') for node in community1) + sum(G.degree(node, weight='weight') for node in community2)
    expected_weight = 0
    for node in community1:
        expected_weight += sum(G[node][neighbor]['weight'] for neighbor in community2) / total_weight
    return expected_weight

def merge_communities(communities, i, j):
    # Merge communities i and j
    communities[i] |= communities[j]
    del communities[j]

希望能对你有所帮助！