如何在 igraph 中运行 louvain 社区检测算法？

Question

谁能给我一个简单的例子，说明如何使用 python 接口在 igraph 中运行 louvain 社区检测算法。有文件吗？

谢谢！

Answer 1

它叫multilevel.community。

根据https://bugs.launchpad.net/igraph/+bug/925038 ...这个功能确实存在它只是称为igraph_community_multilevel

如果您查看 igraph 的 github 存储库

https://github.com/igraph/igraph/blob/master/src/community.c

igraph_community_multilevel 确实存在，它是用 C 语言编写的。我不是 100% 肯定这是你想要的算法，但它可能是。

这是个好消息！谢谢！ 此功能是否导出到 R 中？ 为什么函数具有通用名称（igraph_community_multilevel） 而不是作者给出的名称是（“louvain 方法”）？ 使用名称“louvain”可以让用户更容易找到功能！

Answer 2

这里是如何在 python (igraph,networkx,bct) 的 3 个不同模块中使用 louvain 算法估计模块化 Q。

import numpy as np
import networkx as nx
np.random.seed(9)

# I will generate a stochastic block model using `networkx` and then extract the weighted adjacency matrix.
sizes = [50, 50, 50] # 3 communities
probs = [[0.25, 0.05, 0.02],
         [0.05, 0.35, 0.07],
         [0.02, 0.07, 0.40]]

# Build the model
Gblock = nx.stochastic_block_model(sizes, probs, seed=0)

# Extract the weighted adjacency
W = np.array(nx.to_numpy_matrix(Gblock, weight='weight'))
W[W==1] = 1.5
print(W.shape)
# (150, 150)

#* Modularity estimation using Louvain algorithm 
# 1. `igraph` package

from igraph import *
graph = Graph.Weighted_Adjacency(W.tolist(), mode=ADJ_UNDIRECTED, attr="weight", loops=False)
louvain_partition = graph.community_multilevel(weights=graph.es['weight'], return_levels=False)
modularity1 = graph.modularity(louvain_partition, weights=graph.es['weight'])
print("The modularity Q based on igraph is {}".format(modularity1))

# 2. `networkx`package using `python-louvain`
# https://python-louvain.readthedocs.io/en/latest/

import networkx as nx
import community
G = nx.from_numpy_array(W)
louvain_partition = community.best_partition(G, weight='weight')
modularity2 = community.modularity(louvain_partition, G, weight='weight')
print("The modularity Q based on networkx is {}".format(modularity2))

# 3. `bct` module
# https://github.com/aestrivex/bctpy

import bct
com, q = bct.community_louvain(W)
print("The modularity Q based on bct is {}".format(q))

打印出来：

The modularity Q based on igraph is 0.4257613861340037
The modularity Q based on networkx is 0.4257613861340036
The modularity Q based on bct is 0.42576138613400366