multinetpy 0.1.2

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MultiNetPy

MultiNetPy is a Python package for analyzing multiplex networks. It provides functionalities for importing multiplex network data and computing centrality measures Installation

Install the dependencies using pip: pip install -r requirements.txt

Import a multiplex network dataset

mg = gm.Import_Graph.make_graph( "YourPath_nodes.txt", "YourPath_nodes.edges", "YourPath_nodes_layers.txt" )

Compute centrality measures

First define desires centrality measure, including betweenness and closeness

aggregated_centralities_CC1 = mg.aggregated_CC() weighted_centralities_CC1 = mg.weighted_CC() aggregated_centralities_BC1 = mg.aggregated_BC() weighted_centralities_BC1 = mg.weighted_BC()

Use some other centrality measures in networkX

test = mg.Centralities(nx.degree_centrality) # define desired centrality measure mg.aggregated_centralityTest(nx.degree_centrality)

Code used for comparing a table with calculated centralities

file_path1 =' path_BC.xlsx.' file_path2 ='path_CC.xlsx'

Load the table ranks

table_rank1 = mg.load_table_ranks_from_excel(file_path1) table_rank2 = mg.load_table_ranks_from_excel(file_path2)

kendall's tau

print("kendall's tau in betweenness:") mg.plot_kendall_tau(aggregated_centralities_BC1, weighted_centralities_BC1, table_rank1) print("\nkendall's tau in Closeness Centrality:\n") mg.plot_kendall_tau(aggregated_centralities_CC1, weighted_centralities_CC1, table_rank1)

isim

a, b, c = mg2.intersection_similarity(table_rank2, aggregated_centralities_CC1, weighted_centralities_CC1, max_k=20) print("\nisim in closeness:\n") mg2.display_isim_table(a, b, c)

rank difference

print("\nrank difference in betweenness in your dataset:\n") Rb, R2b, R3b = mg.Rank_Difference(table_rank1, aggregated_centralities_BC1, weighted_centralities_BC1) mg.plot_rank_difference(Rb, R2b, R3b) print("\nrank difference in closeness:\n") R, R2, R3 = mg.Rank_Difference(table_rank2, aggregated_centralities_CC1, weighted_centralities_CC1) mg.plot_rank_difference(R, R2, R3)

Documentation Detailed documentation for each function and class can be found in the source code.