cdlib.algorithms.siblinarity_antichain

siblinarity_antichain(g_original: object, forwards_backwards_on: bool = True, backwards_forwards_on: bool = False, Lambda: int = 1, with_replacement: bool = False) → cdlib.classes.node_clustering.NodeClustering

The algorithm extract communities from a DAG that (i) respects its intrinsic order and (ii) are composed of similar nodes. The approach takes inspiration from classic similarity measures of bibliometrics, used to assess how similar two publications are, based on their relative citation patterns.

Supported Graph Types

Undirected	Directed	Weighted
No	Yes (DAG)	No

Parameters:

• g_original – a networkx/igraph object representing a DAG (directed acyclic graph)
• forwards_backwards_on – checks successors’ similarity. Boolean, default True
• backwards_forwards_on – checks predecessors’ similarity. Boolean, default True
• Lambda – desired resolution of the partition. Default 1
• with_replacement – If True he similarity of a node to itself is equal to the number of its neighbours based on which the similarity is defined. Boolean, default True.

Returns:

NodeClustering object

Example:

>>> from cdlib import algorithms
>>> import networkx as nx
>>> G = nx.karate_club_graph()
>>> coms = algorithms.siblinarity_antichain(G, Lambda=1)

References:

Vasiliauskaite, V., Evans, T.S. Making communities show respect for order. Appl Netw Sci 5, 15 (2020). https://doi.org/10.1007/s41109-020-00255-5

Note

Reference implementation: https://github.com/vv2246/siblinarity_antichains