coach(g_original: object, density_threshold: float = 0.7, affinity_threshold: float = 0.225, closeness_threshold: float = 0.5) → cdlib.classes.node_clustering.NodeClustering

The motivation behind the core-attachment (CoAch) algorithm comes from the observation that protein complexes often have a dense core of highly interactive proteins. CoAch works in two steps, first discovering highly connected regions (“preliminary cores”) of a network and then expanding these regions by adding strongly associated neighbors.

The algorithm operates with three user-specified parameters: minimum core density (for preliminary cores), maximum core affinity (similarity threshold for distinct preliminary cores), and minimum neighbor closeness (for attaching non-core neighbors to preliminary cores).

Supported Graph Types

Undirected Directed Weighted
Yes No No
  • g_original – a networkx/igraph object
  • density_threshold – minimum core density. Default, 0.7
  • affinity_threshold – maximum core affinity. Default, 0.225
  • closeness_threshold – minimum neighbor closeness. Default, 0.5

NodeClustering object

>>> from cdlib import algorithms
>>> import networkx as nx
>>> G = nx.karate_club_graph()
>>> coms =

Wu, M., Li, X., Kwoh, C.-K., Ng, S.-K. A core-attachment based method to detect protein complexes. 2009. In PPI networks. BMC Bioinformatics 10, 169.