cdlib.algorithms.dpclus¶

dpclus
(g_original: object, weights: str = None, d_threshold: float = 0.9, cp_threshold: float = 0.5, overlap: bool = True) → cdlib.classes.node_clustering.NodeClustering¶ DPClus projects weights onto an unweighted graph using a common neighbors approach. In DPClus, the weight of an edge (u, v) is deﬁned as the number of common neighbors between u and v. Similarly, the weight of a vertex is its weighted degree – the sum of all edges connected to the vertex
DPClus does not natively generate overlapping clusters but does allow for overlapping cluster nodes to be added in a postprocessing step.
Supported Graph Types
Undirected Directed Weighted Yes No Yes Parameters:  g_original – a networkx/igraph object
 weights – label used for the edge weights. Default, None.
 d_threshold – cluster density threshold, default 0.9
 cp_threshold – cluster property threshold, default 0.5
 overlap – wheter to output overlapping or crisp communities. Default, True.
Returns: NodeClustering object
Example: >>> from cdlib import algorithms >>> import networkx as nx >>> G = nx.karate_club_graph() >>> coms = algorithms.dpclus(G)
References: AltafUlAmin, M., Shinbo, Y., Mihara, K., Kurokawa, K., Kanaya, S. 2006. Development and implementation of an algorithm for detection of protein complexes in large interaction networks. BMC Bioinformatics 7, 207.
Note
Reference Implementation: https://github.com/trueprice/pythongraphclustering