cdlib.algorithms.lpam

lpam(g_original: object, k: int = 2, threshold: float = 0.5, distance: str = 'amp', seed: int = 0) → cdlib.classes.node_clustering.NodeClustering

Link Partitioning Around Medoids

Supported Graph Types

Undirected Directed Weighted
Yes No No
Parameters:
  • g_original – a networkx/igraph object
  • k – number of clusters
  • threshold – merging threshold in [0,1], default 0.5
  • distance – type of distance: “amp” - amplified commute distance, or “cm” - commute distance, or distance matrix between all edges as np ndarray
  • seed – random seed for k-medoid heuristic
Returns:

NodeClustering object

Example:
>>> from cdlib import algorithms
>>> import networkx as nx
>>> G = nx.karate_club_graph()
>>> coms = algorithms.lpam(G, k=2, threshold=0.4, distance = "amp")
References:

Alexander Ponomarenko, Leonidas Pitsoulis, Marat Shamshetdinov. “Link Partitioning Around Medoids”. https://arxiv.org/abs/1907.08731