ilouvain(g_original: object, labels: dict) → cdlib.classes.attr_node_clustering.AttrNodeClustering

The I-Louvain algorithm extends the Louvain approach in order to deal only with the scalar attributes of the nodes. It optimizes Newman’s modularity combined with an entropy measure.

Supported Graph Types

Undirected Directed Weighted Temporal Node Attribute
Yes No No No Yes
  • g_original – a networkx/igraph object
  • labels – dictionary specifying for each node (key) a dict (value) specifying the name attribute (key) and its value (value)

AttrNodeClustering object

>>> from cdlib.algorithms import ilouvain
>>> import networkx as nx
>>> import random
>>> l1 = [0.1, 0.4, 0.5]
>>> l2 = [34, 3, 112]
>>> g_attr = nx.barabasi_albert_graph(100, 5)
>>> labels=dict()
>>> for node in g_attr.nodes():
>>>    labels[node]={"l1":random.choice(l1), "l2":random.choice(l2)}
>>> id = dict()
>>> for n in g.nodes():
>>>     id[n] = n
>>> communities = ilouvain(g_attr, labels, id)

Combe D., Largeron C., Géry M., Egyed-Zsigmond E. “I-Louvain: An Attributed Graph Clustering Method”. <> In: Fromont E., De Bie T., van Leeuwen M. (eds) Advances in Intelligent Data Analysis XIV. IDA (2015). Lecture Notes in Computer Science, vol 9385. Springer, Cham