community.partition_aggregation: Partition aggregation based community detection algorithm

In Issamfalih/MUNA: MUNA: A Multiplex Network Analysis Library

Description

This method applies a partition aggregation based community detection algorithm. The idea is to apply a standard community detection algorithm designed for monoplex network (community_algo) to each layer of the multiples. Then an ensemble clustering approach is applied on the obtained clusterings in order to compute the final community structure. The CSPA ensemble clustering approach is used for that purpose. All basic community detection approaches provided in igraph can be used here.

Usage

community.partition_aggregation(multiplex, community_algorithm, h, alpha)

Arguments

multiplex	: The multiplex object.
community_algorithm	: String, the name of classical community detection algorithm. As Licod, Louvain, Infomap, Walktrap,...
h	:
alpha	:

Value

returns an igraph communities object, please see igraph manual page for details.

Author(s)

Issam Falih <[e-mail]>

Examples

M <- Multiplex_Lazega
community.partition_aggregation(M)

Issamfalih/MUNA documentation built on May 8, 2019, 11:52 a.m.