lfm(g_original: object, alpha: float) → cdlib.classes.node_clustering.NodeClustering¶
LFM is based on the local optimization of a fitness function. It finds both overlapping communities and the hierarchical structure.
Supported Graph Types
Undirected Directed Weighted Yes No No Parameters:
- g_original – a networkx/igraph object
- alpha – parameter to controll the size of the communities: Large values of alpha yield very small communities, small values instead deliver large modules. If alpha is small enough, all nodes end up in the same cluster, the network itself. In most cases, for alpha < 0.5 there is only one community, for alpha > 2 one recovers the smallest communities. A natural choise is alpha =1.
>>> from cdlib import algorithms >>> import networkx as nx >>> G = nx.karate_club_graph() >>> com = algorithms.lfm(G, alpha=0.8)
Lancichinetti, Andrea, Santo Fortunato, and János Kertész. Detecting the overlapping and hierarchical community structure in complex networks New Journal of Physics 11.3 (2009): 033015.