dcd-lago 1.0.0

Python implementation of the LAGO method for dynamic community detection in temporal networks. Derived functions such as the Longitudinal Modularity are also provided.

Dynamic Community Detection

This repository compiles methods and experiments related to community structures in temporal networks.

The current code corresponds to the pre-print Longitudinal Modularity, a Modularity for Link Streams.

Example

To compute the Longitudinal Modularity of a dynamic community structure on a temporal network:

import longitudinal_modularity_tools as lmt


longitudinal_modularity = get_longitudinal_modularity(
    time_links_df=time_links_df, 
    # (pd.DataFrame) Interactions table. Columns: source, target, time, source_commu, target_commu
    communities_df=communities_df, 
    # (pd.DataFrame): Nodes communities memberships. Columns: node, time, commu
    expectation=expectation, 
    # (str, optional): Logitudinal expectation type. Must be "cm" for co-membership, "jm" for joint membership, or "mm" for mean membership. Defaults to "mm".
    omega=omega,
    # (float, optional): Time penalty weigth. High values will favor continuous communities. Defaults to 1.
)