def infomap_find_communities(input_file, n_trials):
"""
Partition network with the Infomap algorithm.
Annotates nodes with 'community' id and return number of communities found.
:param input_file: Input file with data
:param n_trials: Number of trials options for infomap
:rtype: Total number of communities, python dictionary of detected communities
"""
options = '--two-level -z' + ' -N ' + n_trials
print('Number of trials: {}'.format(n_trials), log_type='info')
# Create Infomap wrapper
infomap_wrapper = infomap.Infomap(options)
# print("Building Infomap network from a NetworkX graph.....", log_type='info')
# for e in graph.edges():
# infomap_wrapper.addLink(*e)
print('Building Infomap network from the input file.....', log_type='info')
infomap_wrapper.readInputData(input_file)
tree = run_algorithm(infomap_wrapper)
# Find communities
communities = {}
for node in tree.leafIter():
communities[node.originalLeafIndex] = node.moduleIndex()
# nx.set_node_attributes(graph, name='community', values=communities)
# return number of modules found
return tree.numTopModules(), communities
def findCommunities(G):
"""
Partition network with the Infomap algorithm.
Annotates nodes with 'community' id and return number of communities found.
"""
infomapWrapper = infomap.Infomap("--two-level")
print("Building Infomap network from a NetworkX graph...")
for e in G.edges():
infomapWrapper.addLink(*e)
print("Find communities with Infomap...")
infomapWrapper.run()
tree = infomapWrapper.tree
print("Found %d top modules with codelength: %f" %
(tree.numTopModules(), tree.codelength()))
communities = {}
for node in tree.leafIter():
communities[node.originalLeafIndex] = node.moduleIndex()
nx.set_node_attributes(G, name='community', values=communities)
return tree.numTopModules()
def runInfomapAsLibrary(input, infomapArgs):
if (noLibrary):
sys.exit("Infomap library not available")
infomapWrapper = infomap.Infomap("{} {}".format(input, infomapArgs), True)
infomapWrapper.run()
return infomapWrapper.codelength()
id2word = word2vec.wv.index2word[:num_words]
word2id = {j: i for i, j in enumerate(id2word)}
links = {}
# 每个词找与它相似度不小于0.6的词（不超过50个），来作为图上的边
for i in tqdm(range(num_words)):
sims = np.dot(word_vecs, word_vecs[i])
idxs = sims.argsort()[::-1][1:]
for j in idxs[:50]:
if sims[j] >= min_sim:
links[(i, j)] = float(sims[j])
else:
break
infomapWrapper = infomap.Infomap("--two-level --directed")
# 如果重叠社区发现，则只需要：
# infomapWrapper = infomap.Infomap("--two-level --directed --overlapping")
for (i, j), sim in tqdm(links.items()):
_ = infomapWrapper.addLink(i, j, sim)
infomapWrapper.run()
tree = infomapWrapper.tree
word2class = {}
class2word = {}
for node in tree.leafIter():
if id2word[node.physIndex] not in word2class:
word2class[id2word[node.physIndex]] = []
word2class[id2word[node.physIndex]].append(node.moduleIndex())
from infomap import infomap
name = "ninetriangles"
filename = "../../{}.net".format(name)
infomapWrapper = infomap.Infomap("-N5 --silent")
infomapWrapper.readInputData(filename)
infomapWrapper.run()
tree = infomapWrapper.tree
print("Found %d top modules with codelength: %f" %
(tree.numTopModules(), tree.codelength()))
print("Writing top level modules to %s_level1.clu..." % name)
tree.writeClu("%s_level1.clu" % name, 1)
print("Writing second level modules to %s_level2.clu..." % name)
tree.writeClu("%s_level2.clu" % name, 2)
print("Writing tree to %s.tree..." % name)
tree.writeHumanReadableTree("%s.tree" % name)
print("\nModules at depth 1:\n#node module")
for node in tree.leafIter(1):
print("%d %d" % (node.physIndex, node.moduleIndex()))
print("\nModules at depth 2:\n#node module")
for node in tree.leafIter(2):
print("%d %d" % (node.physIndex, node.moduleIndex()))
# nx.layou
#first compute the best partition
# louvain
partition = community.best_partition(G, resolution=0.2)
# dendogram = community.generate_dendrogram(G, resolution=0.2)
# partition = community.partition_at_level(dendogram, 1)
#drawing
size = float(len(set(partition.values())))
pos = nx.kamada_kawai_layout(G)
# infomap
infomapWrapper = infomap.Infomap("--two-level")
print("Building Infomap network from a NetworkX graph...")
for e in G.edges():
infomapWrapper.addLink(*e)
print("Find communities with Infomap...")
infomapWrapper.run()
tree = infomapWrapper.tree
print("Found %d top modules with codelength: %f" %
(tree.numTopModules(), tree.codelength()))
partition = {}
for node in tree.leafIter():