cdlib.algorithms.ilouvain

cdlib.algorithms.ilouvain(g_original: object, labels: dict) → 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

Parameters:

• 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)

Returns:

AttrNodeClustering object

Example:

>>> 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)

References:

Combe D., Largeron C., Géry M., Egyed-Zsigmond E. “I-Louvain: An Attributed Graph Clustering Method”. <https://link.springer.com/chapter/10.1007/978-3-319-24465-5_16> 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