cdlib.algorithms.lpam¶
- cdlib.algorithms.lpam(g_original: object, k: int = 2, threshold: float = 0.5, distance: str = 'amp', seed: int = 0) 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