def get_infomap_communities(graph: nx.Graph, reddit_edge_weight=None):
im = Infomap("--flow-model undirected -N 10 --prefer-modular-solution")
## im only works with numerical ids, so we need to save a mapping
ids_to_names = {}
names_to_ids = {}
for index, node in enumerate(graph.nodes):
ids_to_names[index] = node
names_to_ids[node] = index
im.add_node(index, name=node)
# iterate over edges and add them to the im tree, optionally adding the weight
for e1, e2, data in graph.edges(data=True):
e1_id = names_to_ids[e1]
e2_id = names_to_ids[e2]
weight = data[reddit_edge_weight] if reddit_edge_weight else None
link = (e1_id, e2_id, weight) if weight else (e1_id, e2_id)
im.add_link(*link)
im.run()
for node in im.tree:
if node.is_leaf:
graph.nodes[ids_to_names[node.node_id]][
"infomap_community"
] = node.module_id
return graph
def get_benchmark_amis(G,gt):
# Louvain
louv = community.best_partition(G)
louvc = []
for idx,val in louv.items():
louvc.append(val)
louv_ami = metrics.adjusted_mutual_info_score(gt,louvc)
# Fluid communities
fluid = asyn_fluidc(G,2)
list_nodes = [set(c) for c in fluid]
est_idx = np.zeros((nx.number_of_nodes(G),))
for i in range(len(list_nodes)):
for idx in list_nodes[i]:
est_idx[idx] = i
fluid_ami = metrics.adjusted_mutual_info_score(gt,est_idx)
# FastGreedy
list_nodes = list(greedy_modularity_communities(G))
est_idx = np.zeros((nx.number_of_nodes(G),))
for i in range(len(list_nodes)):
for idx in list_nodes[i]:
est_idx[idx] = i
fg_ami = metrics.adjusted_mutual_info_score(gt,est_idx)
# Infomap
im = Infomap()
for node in G.nodes:
im.add_node(node)
for edge in G.edges:
im.add_link(edge[0], edge[1])
im.add_link(edge[1],edge[0])
# Run the Infomap search algorithm to find optimal modules
im.run()
# print(f"Found {im.num_top_modules} modules with Infomap")
est_idx = np.zeros((nx.number_of_nodes(G),))
for node in im.tree:
if node.is_leaf:
est_idx[node.node_id] = node.module_id
im_ami = metrics.adjusted_mutual_info_score(gt,est_idx)
benchmark = {'Louvain':louv_ami,
'Fluid':fluid_ami,
'FastGreedy':fg_ami,
'Infomap':im_ami}
return benchmark
for idx in list_nodes:
est_idx[idx] = com
runtime = time.time() - time_s
mutual_info = metrics.adjusted_mutual_info_score(database['labels'], est_idx)
scores['louvain-noisy'] = mutual_info
runtimes['louvain-noisy'] = runtime
###########################################################
###########################################################
# Method: Infomap
###########################################################
# Raw
time_s = time.time()
im = Infomap()
for node in G.nodes:
im.add_node(node)
for edge in G.edges:
im.add_link(edge[0], edge[1])
im.add_link(edge[1], edge[0])
# Run the Infomap search algorithm to find optimal modules
im.run()
# print(f"Found {im.num_top_modules} modules with Infomap")
est_idx = np.zeros((num_nodes, ))
for node in im.tree:
if node.is_leaf:
est_idx[node.node_id] = node.module_id
runtime = time.time() - time_s
mutual_info = metrics.adjusted_mutual_info_score(database['labels'], est_idx)
scores['infomap-raw'] = mutual_info
runtimes['infomap-raw'] = runtime
from infomap import Infomap
im = Infomap(two_level=True, silent=True)
# Optionally add nodes with names
im.add_node(0, "Node 0")
im.add_node(1, "Node 1")
# Adding links automatically create nodes if not exist.
# Optionally add weight as third argument
im.add_link(0, 1)
im.add_link(0, 2)
im.add_link(0, 3)
im.add_link(1, 0)
im.add_link(1, 2)
im.add_link(2, 1)
im.add_link(2, 0)
im.add_link(3, 0)
im.add_link(3, 4)
im.add_link(3, 5)
im.add_link(4, 3)
im.add_link(4, 5)
im.add_link(5, 4)
im.add_link(5, 3)
print("Run Infomap...")
im.run()
print(
f"Found {im.num_top_modules} modules with codelength {im.codelength:.8f} bits"