igraph plot for multiple layers partition does not return expected output

[balandongiv]

Hi @vtraag, thanks for responding.

@vtraag

Could you indicate what output you would expect exactly?

Sorry for the lack of information provided, but previously I had to trim some content (i.e., image) due to the new member requirement.

Actually, I desire to reproduce the figure in the slice to layers example (describe in leidenalg-multiplex section), as shown below

Here.the node interaction for each of the 3 time slices are maintained and were map using the numpy array (A1,A2,A3 -in the code provided above)

@vtraag

Please note that this may be incorrect. You try to detect clusters in the overall graph containing both all layers and the interslice coupling links. You typically want to use a resolution parameter of 0 for the interslice coupling links however. Please see the [documentation] and the included example for more details. Note that there’s also the simpler [ find_partition_temporal ], which does most of this stuff automatically correctly.

Given that I would like to reproduce the above figure, I assume we need to time_slices_to_layers rather than directly used find_partition_temporal as you suggested. Hence, I change the code into

gamma=0
optimiser = la.Optimiser()
layers, interslice, G = la.time_slices_to_layers ( [G_1, G_2, G_3], interslice_weight=1)

partitions = [la.CPMVertexPartition(H, node_sizes='node_size',weights='weight',
                                    resolution_parameter=gamma) for H in layers];

interslice_partition = la.CPMVertexPartition(interslice, resolution_parameter=gamma,
                                             node_sizes='node_size', weights='weight');

diff = optimiser.optimise_partition_multiplex(partitions + [interslice_partition]);

ig.plot(diff,vertex_label = G.vs['id'])

However, ig.plot(diff,vertex_label = G.vs['id']) or ig.plot(diff) return a white blank figure.

The full code are provided below.

p.s. I cannot hide my excitement as the author himself reply this OP

import leidenalg as la
import igraph as ig
import numpy as np


A1 = np.array ( [[0., 0., 0., 0., 0,0,0],[5., 0., 0., 0., 0,0,0],[1., 0., 0., 0., 0,0,0],
                 [0., 1., 2., 0., 0,0,0],[0., 0., 0., 1., 0,0,0], [0., 0., 0., 1., 0,0,0],
                 [0., 0., 0., 0., 1,1,0]] )


A2 = np.array ( [[0., 0., 0., 0., 0,0,0],[5., 0., 0., 0., 0,0,0], [1., 0., 0., 0., 0,0,0],
                 [0., 1., 2., 0., 0,0,0], [0., 0., 0., 1., 0,0,0],[0., 0., 1., 1., 0,0,0],
                 [0., 0., 0., 0., 1,1,0]] )

A3 = np.array ( [[0., 0., 0., 0., 0,0,0], [0., 0., 0., 0., 0,0,0],[0., 0., 0., 0., 0,0,0],
                 [0., 1., 2., 0., 0,0,0],[0., 0., 0., 1., 0,0,0],[0., 0., 0., 1., 0,0,0],
                 [0., 0., 0., 0., 1,1,0]] )


G_1 = ig.Graph.Weighted_Adjacency ( A1.tolist () )
G_2 = ig.Graph.Weighted_Adjacency ( A2.tolist () )
G_3 = ig.Graph.Weighted_Adjacency ( A3.tolist () )
G_1.vs ['id'] = ['A','B','C','D','E','F','G']
G_2.vs ['id'] = ['A','B','C','D','E','F','G']
G_3.vs ['id'] = ['A','B','C','D','E','F','G']

gamma=0
optimiser = la.Optimiser()
layers, interslice, G = la.time_slices_to_layers ( [G_1, G_2, G_3], interslice_weight=1)

partitions = [la.CPMVertexPartition(H, node_sizes='node_size',weights='weight',
                                    resolution_parameter=gamma) for H in layers];

interslice_partition = la.CPMVertexPartition(interslice, resolution_parameter=gamma,
                                             node_sizes='node_size', weights='weight');

diff = optimiser.optimise_partition_multiplex(partitions + [interslice_partition]);

ig.plot(diff,vertex_label = G.vs['id'])