ERIS: Polarization, Antagonism and Community Boundaries

Description is coming.

You can find extra resources related to execution times on large graphs in experiment_complexity/.

To use the code in R

2.1 With igraph library :

Build your graph.
Use a community detection algorithm.
Extract communities membership (function membership in igraph)
Add to each vertex its community identifier in an attribute (V(g)$community = membership(g))
Call the function compute_polarization_metrics_graph(your_graph, community_attr = your_community_attr_name, vnames_attr = your_vertex_name_attr)
Use the returned indicatives ($antagonism_vertices, $boundary_sizes_matrix, $porosity_matrix, $antagonism_matrix, $vizualisation)

2.2 Without igraph library :

Build your graph's adjacency matrix.
Use a community detection algorithm.
Extract community membership for each vertex.
Build a binary community matrix where 0 means that the vertex heading the row is not in the community heading the column, and 1 otherwise.
Call the function compute_polarization_metrics(your_adjacency_matrix your_community_matrix, vertices_names = vector_with_all_vertices_names, community_names = vector_with_all_community_names)
Use the returned indicatives ($antagonism_vertices, $boundary_sizes_matrix, $porosity_matrix, $antagonism_matrix, $vizualisation)