Module multilayer_network#

What is a multilayer network?#

Multilayer nNtwork is a class to extend functionality of networkx.Graph library to store and manipulate multilayer networks, which are a fundamental structure in the library. Module also allows to read network from mpx text files which stores such a structures.

Available data#

Here is an exemplar repository with multilayer networks: hub, but you find them in many other sited around Internet.

Example of usage#

Let’s crete some multilayer networks in several ways.

  1. By defining separate graphs and layer names:

    from network_diffusion.mln.mlnetwork import MultilayerNetwork
import networkx as nx

M = [nx.les_miserables_graph(), nx.les_miserables_graph(), nx.les_miserables_graph()]

mpx = MultilayerNetwork.from_nx_layers(M)
mpx.describe()

============================================
network parameters
--------------------------------------------
general parameters:
    number of layers: 3
    multiplexing coefficient: 1.0
layer 'layer_0' parameters:
    graph type - <class 'networkx.classes.graph.Graph'>
    number of nodes - 77
    number of edges - 254
    average degree - 6.5974
    clustering coefficient - 0.5731
layer 'layer_1' parameters:
    graph type - <class 'networkx.classes.graph.Graph'>
    number of nodes - 77
    number of edges - 254
    average degree - 6.5974
    clustering coefficient - 0.5731
layer 'layer_2' parameters:
    graph type - <class 'networkx.classes.graph.Graph'>
    number of nodes - 77
    number of edges - 254
    average degree - 6.5974
    clustering coefficient - 0.5731
============================================

  1. By defining separate graphs and using default names of layers:

    from network_diffusion.mln.mlnetwork import MultilayerNetwork
import networkx as nx
M = [nx.les_miserables_graph(), nx.les_miserables_graph(), nx.les_miserables_graph()]

mpx = MultilayerNetwork.from_nx_layer(M, ['A', 'B', 'C'])
mpx.describe()

============================================
network parameters
--------------------------------------------
general parameters:
    number of layers: 3
    multiplexing coefficient: 1.0
layer 'A' parameters:
    graph type - <class 'networkx.classes.graph.Graph'>
    number of nodes - 77
    number of edges - 254
    average degree - 6.5974
    clustering coefficient - 0.5731
layer 'B' parameters:
    graph type - <class 'networkx.classes.graph.Graph'>
    number of nodes - 77
    number of edges - 254
    average degree - 6.5974
    clustering coefficient - 0.5731
layer 'C' parameters:
    graph type - <class 'networkx.classes.graph.Graph'>
    number of nodes - 77
    number of edges - 254
    average degree - 6.5974
    clustering coefficient - 0.5731
============================================

  1. By reading out mpx file:

    mpx = MultilayerNetwork.from_mpx('/my_project/monastery.mpx')
mpx.describe()

============================================
network parameters
--------------------------------------------
general parameters:
    number of layers: 10
    multiplexing coefficient: 0.7778
layer 'like1' parameters:
    graph type - <class 'networkx.classes.digraph.DiGraph'>
    number of nodes - 18
    number of edges - 55
    average degree - 6.1111
    clustering coefficient - 0.1732
layer 'like2' parameters:
    graph type - <class 'networkx.classes.digraph.DiGraph'>
    number of nodes - 18
    number of edges - 57
    average degree - 6.3333
    clustering coefficient - 0.2923
layer 'like3' parameters:
    graph type - <class 'networkx.classes.digraph.DiGraph'>
    number of nodes - 18
    number of edges - 56
    average degree - 6.2222
    clustering coefficient - 0.3603
layer 'dislike' parameters:
    graph type - <class 'networkx.classes.digraph.DiGraph'>
    number of nodes - 17
    number of edges - 47
    average degree - 5.5294
    clustering coefficient - 0.1213
layer 'esteem' parameters:
    graph type - <class 'networkx.classes.digraph.DiGraph'>
    number of nodes - 18
    number of edges - 54
    average degree - 6.0
    clustering coefficient - 0.3222
layer 'desesteem' parameters:
    graph type - <class 'networkx.classes.digraph.DiGraph'>
    number of nodes - 17
    number of edges - 58
    average degree - 6.8235
    clustering coefficient - 0.2029
layer 'positive_influence' parameters:
    graph type - <class 'networkx.classes.digraph.DiGraph'>
    number of nodes - 18
    number of edges - 53
    average degree - 5.8889
    clustering coefficient - 0.3537
layer 'negative_influence' parameters:
    graph type - <class 'networkx.classes.digraph.DiGraph'>
    number of nodes - 18
    number of edges - 50
    average degree - 5.5556
    clustering coefficient - 0.1084
layer 'praise' parameters:
    graph type - <class 'networkx.classes.digraph.DiGraph'>
    number of nodes - 18
    number of edges - 39
    average degree - 4.3333
    clustering coefficient - 0.3048
layer 'blame' parameters:
    graph type - <class 'networkx.classes.digraph.DiGraph'>
    number of nodes - 15
    number of edges - 41
    average degree - 5.4667
    clustering coefficient - 0.1133
============================================