cdlib.algorithms.coach

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

Parameters:

• 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

Returns:

NodeClustering object

Example:

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

References:

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.

Note

Reference Implementation: https://github.com/trueprice/python-graph-clustering