多层分区的 igraph 图没有 return 预期输出

Question

假设有 3 个时间片（即 G_1、G_2、G_3）通过 time_slices_to_layers 转换为 layers。然后对这些层和中间切片进行 find_partition 处理以获得它们的 communities。但是，绘制这些分区不会产生预期的输出。

这里每个切片的节点交互如下图所示

然而，绘制分区结果给出了不正确的 intraslice link 连接。

虽然interslice coupling应该是随机的，但我希望intralsice link应该保持如下

请问如何解决这个问题。

我怀疑这是由于错误分配了 vertex_label

ig.plot(partition,vertex_label = ['A','B','C','D','E','F','G'])

或者与其他配置有关？

重现上图的完整代码

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']

G_layers, G_interslice, G = la.time_slices_to_layers ( [G_1, G_2, G_3], interslice_weight=1,
                                                       slice_attr='slice', vertex_id_attr='id',
                                                       edge_type_attr='type', weight_attr='weight' )

partition = la.find_partition(G, la.CPMVertexPartition,resolution_parameter = 0.05);
ig.plot(partition,vertex_label = ['A','B','C','D','E','F','G'])

Cross-posted on igraph forum

Answer 1

详细解释请注明出处Vincent Traag。

根据创作者反馈，上述问题可按如下方式解决

# Convert from slices to layers
G_layers, G_interslice, G = la.time_slices_to_layers([G_1, G_2, G_3], interslice_weight=1e-1,
                                                     slice_attr='slice', vertex_id_attr='id')
# Create partitions
gamma = 0.3
partitions = [la.CPMVertexPartition(H, node_sizes='node_size',weights='weight',
                                    resolution_parameter=gamma) for H in G_layers]
interslice_partition = la.CPMVertexPartition(G_interslice, resolution_parameter=0,
                                             node_sizes='node_size', weights='weight')


# Detect communities
optimiser = la.Optimiser()
optimiser.set_rng_seed(11)
diff = optimiser.optimise_partition_multiplex(partitions + [interslice_partition])


# Plot network
partition_all = ig.VertexClustering(G, partitions[0].membership)
ig.plot(partition_all,
        vertex_label = [f'{v["id"]}-{v["slice"]}' for v in G.vs])

产生了

可以在 igraph-forum

中找到深入对话

多层分区的 igraph 图没有 return 预期输出

igraph plot for multiple layers partition does not return expected output

python

igraph