leiden_workflow: Add Leiden Communities to graph
In agoutsmedt/networkflow: Functions For A Workflow To Manipulate Networks

leiden_workflow

R Documentation

Add Leiden Communities to graph

Description

This function has been superseded by the more general networkflow::add_clusters(). This function takes as input a tidygraph graph It then runs the Leiden detection community algorithm \insertCitetraag2019networkflow which creates a partition. The Leiden_workflow functhion uses find_partition() from the leidenAlg package. The function then associates each node to its corresponding community number. It also creates a community attribute for edges: to each edge is associated a corresponding community number, if the two nodes connected by the edge belong to the same community. If nodes have a different community, the edge takes as attribute the total number of communities plus 1.

Usage

leiden_workflow(graph, res_1 = 1, res_2 = NULL, res_3 = NULL, niter = 1000)

Arguments

`graph`	A tidygraph object with a "weight" column for edges.
`res_1`	The first resolution used for running the Leiden algorithm. 1 by default.
`res_2`	The second resolution used for running the Leiden algorithm a second time. It adds a second community attribute to nodes and edges. By default, res_2 is null and the function just run the Leiden algorithm once (with a resolution equals to res_1).
`res_3`	The third resolution used for running the Leiden algorithm a third time.
`niter`	Number of iterations to run the Leiden algorithm, in order to optimise the resulting partition. By default, `niter` equals 500 which warrants a quasi-optimal partitionning. Decrease n_iterations for exploratory work, in order to decrease computation time.

Details

The function could be run for 1, 2 or 3 different resolution values of the Leiden algorithm. It allows the user to compare different communities partitions. A low resolution means fewer communities. For instance, if the second resolution is smaller than the first one, we can observe how decreasing the resolution led some communities to disappear and to be merged with other communities. Sankey diagrams enable interesting analysis of the different partitions.

The function also automatically calculates the percentage of total nodes that are gathered in each community, in the column Size_com. This calculation is only done for Com_ID, that is for the resolution equals to 1.

To make plotting easier later, a zero is put before each one-digit community number (community 5 becomes 05).

The community attribute of nodes and edges for the first resolution is called Com_ID. For the second and third resolution, it is called respectively Com_ID_2 and Com_ID_3.

Attributing a community number to edges enable to give edges the same color of the nodes they are connecting, if the two nodes have the same color, or a different color than any node, if the nodes belong to a different community.