![[Deprecated]](figures/lifecycle-deprecated.svg)
This function has been superseded by the more general networkflow::add_clusters().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
(Traag et al. 2019)
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.
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,
niterequals 500 which warrants a quasi-optimal partitionning. Decrease n_iterations for exploratory work, in order to decrease computation time.
Value
The same tidygraph graph as input, but with a new (or three) community number attribute(s) for each node and each edge.
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.