def main(): # Load graph twitter_graph = nx.read_edgelist( "twitter_congress_graph.edgelist", delimiter="\t", nodetype=str, create_using=nx.DiGraph() ) twitter_graph.remove_edges_from(twitter_graph.selfloop_edges()) # Find largest connected component twitter_mc = nx.weakly_connected_component_subgraphs(twitter_graph)[0] # Take 2-core of main component mc_core = core.k_core(twitter_mc, 2) # Output results nx.write_edgelist(twitter_graph, "twitter_congress_clean", delimiter="\t") nx.write_edgelist(twitter_mc, "twitter_congress_mc.edgelist", delimiter="\t") nx.write_edgelist(mc_core, "twitter_congress_mc_2core.edgelist", delimiter="\t")
def main(): # Load graph twitter_graph = nx.read_edgelist('twitter_congress_graph.edgelist', delimiter="\t", nodetype=str, create_using=nx.DiGraph()) twitter_graph.remove_edges_from(twitter_graph.selfloop_edges()) # Find largest connected component twitter_mc = nx.weakly_connected_component_subgraphs(twitter_graph)[0] # Extract chamber-specific networks rep_network = congressSubgraph(twitter_graph, title='Rep') sen_network = congressSubgraph(twitter_graph, title='Sen') # Take 2-core of main component mc_core = core.k_core(twitter_mc, 2) # Output results nx.write_edgelist(twitter_graph, 'twitter_congress_clean.edgelist', delimiter='\t', data=False) nx.write_edgelist(twitter_mc, 'twitter_congress_mc.edgelist', delimiter='\t', data=False) nx.write_edgelist(mc_core, 'twitter_congress_mc_2core.edgelist', delimiter='\t', data=False) nx.write_edgelist(rep_network, 'twitter_representative_mc.edgelist', delimiter='\t', data=False) nx.write_edgelist(sen_network, 'twitter_senators_mc.edgelist', delimiter='\t', data=False)