Eva - Community Discovery for Labeled graphs.

Citation

If you use our algorithm please cite the following works:

Citraro S., Rossetti G. (2020) Eva: Attribute-Aware Network Segmentation. In: Cherifi H., Gaito S., Mendes J., Moro E., Rocha L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_12

Citraro, S., Rossetti, G. Identifying and exploiting homogeneous communities in labeled networks. Appl Netw Sci 5, 55 (2020). https://doi.org/10.1007/s41109-020-00302-1

Installation

In order to install the package just download (or clone) the current project and copy the demon folder in the root of your application.

Alternatively use pip:

sudo pip install eva_lcd

Eva is written in python and requires the following package to run:

• networkx
• numpy

Execution

Eva can be executed specifying as input:

• a (labeled) networkx Graph object
• the value of the trade-off parameter (alpha)

import networkx as nx
from Eva import eva_best_partition, modularity, purity

g = nx.karate_club_graph()
part, com_labels = eva_best_partition(g, alpha=0.8)

q = modularity(part, g)
p = purity(com_labels)

An example of execution is also showed in the file "EVA_Example.ipynb"