screenplay_network_viz.py 文件源码

def graph_info(g):
    result = {}
    components = list(nx.strongly_connected_component_subgraphs(g))
    in_degrees = g.in_degree()
    out_degrees = g.out_degree()
    highest_in_degree_node = sorted(in_degrees, key = lambda x: in_degrees[x], reverse = True)[0]
    highest_out_degree_node = sorted(out_degrees, key = lambda x: out_degrees[x], reverse = True)[0]

    result['highest in_degree node'] = highest_in_degree_node
    result['highest out_degree_node'] = highest_out_degree_node

    result['numnber of components'] = len(components)
    result['number of nodes'] = g.number_of_nodes()
    result['number of edges'] = g.number_of_edges()
#Degree centrality
    in_degree_centrality = nx.in_degree_centrality(g)
    out_degree_centrality = nx.out_degree_centrality(g)
    result['sorted in_degree centrality'] = sorted([(el,in_degree_centrality[el]) for el in g.nodes()], key = lambda x: x[1], reverse = True)
    result['sorted out_degree centrality'] = sorted([(el,out_degree_centrality[el]) for el in g.nodes()], key = lambda x: x[1], reverse = True)

    result['closeness_centrality'] = sorted([(el,nx.closeness_centrality(g)[el]) for el in nx.closeness_centrality(g)], key = lambda x: x[1], reverse = True)
    result['highest in_degree node closeness'] = nx.closeness_centrality(g)[highest_in_degree_node]
    result['highest out_degree node closeness'] = nx.closeness_centrality(g)[highest_out_degree_node]


    result['betweenness centrality'] = sorted([(el,nx.betweenness_centrality(g)[el]) for el in nx.betweenness_centrality(g)], key = lambda x: x[1], reverse = True)
    result['highest in_degree node betweenness'] = nx.betweenness_centrality(g)[highest_in_degree_node]
    result['highest in_degree node betweenness'] = nx.betweenness_centrality(g)[highest_out_degree_node]


    largest_component = sorted (components, key = lambda x: x.number_of_nodes(), reverse = True)[0]

    result['largest strongly component percent'] = largest_component.number_of_nodes()/float(g.number_of_nodes())
    result['largest strongly component diameter'] = nx.diameter(largest_component)
    result['largest strongly component average path length'] = nx.average_shortest_path_length(largest_component)
    result['average_degree (undireceted)'] = sum(g.degree().values())/float(g.number_of_nodes())
    result['avg_cluster_coefficient (transitivity)'] = nx.transitivity(g)
    return result