# Start writing code here... !pip install networkx==2.6.3 import csv from operator import itemgetter import networkx as nx from networkx.algorithms import community

with open('node.csv', 'r') as nodecsv: nodereader = csv.reader(nodecsv) # Read the node nodes = [n for n in nodereader][1:] node_id = [n[0] for n in nodes] # 节点id是唯一标识 with open('edge.csv', 'r') as edgecsv: edgereader = csv.reader(edgecsv) # Read the edge edges = [tuple(e) for e in edgereader][1:] # Retrieve the data

edges

print(len(node_id))

print(len(edges))

G = nx.Graph() # Initialize a Graph object G.add_nodes_from(node_id) # Add nodes to the Graph G.add_edges_from(edges) # Add edges to the Graph print(nx.info(G)) # Print information about the Graph

# Create an empty dictionary for each attribute cp_dict = {} lb_dict = {} for node in nodes: # Loop through the list of nodes, one row at a time cp_dict[node[0]] = node[1] lb_dict[node[0]] = node[2] # Add each dictionary as a node attribute to the Graph object nx.set_node_attributes(G, cp_dict, 'caipin') nx.set_node_attributes(G, lb_dict, 'label') # # Loop through each node, to access and print all the "birth_year" attributes # for n in G.nodes(): # print(n, G.nodes[n]['caipin'])

for n in G.nodes(): print(n, G.nodes[n]['label'])

density = nx.density(G)#网络密度 print("Network density:", density)

shrimp_cabage_path = nx.shortest_path(G, source='6', target='83')# shrimp和Chinese carbage之间的最短路径 print("Shortest path between pork and wine:", shrimp_cabage_path) print("Length of that path:", len(shrimp_cabage_path)-1)

print(nx.is_connected(G)) # 找到最大连通图 components = nx.connected_components(G) largest_component = max(components, key=len) # Create a "subgraph" of just the largest component # Then calculate the diameter of the subgraph, just like you did with density. # subgraph = G.subgraph(largest_component) diameter = nx.diameter(subgraph) print("Network diameter of largest component:", diameter)

triadic_closure = nx.transitivity(G) print("Triadic closure:", triadic_closure)

degree_dict = dict(G.degree(G.nodes())) nx.set_node_attributes(G, degree_dict, 'degree')

print(G.nodes['20'])

sorted_degree = sorted(degree_dict.items(), key=itemgetter(1), reverse=True) print("Top 20 nodes by degree:") for d in sorted_degree[:20]: print(d)

betweenness_dict = nx.betweenness_centrality(G) # Run betweenness centrality eigenvector_dict = nx.eigenvector_centrality(G) # Run eigenvector centrality # Assign each to an attribute in your network nx.set_node_attributes(G, betweenness_dict, 'betweenness') nx.set_node_attributes(G, eigenvector_dict, 'eigenvector') sorted_betweenness = sorted(betweenness_dict.items(), key=itemgetter(1), reverse=True) print("Top 20 nodes by betweenness centrality:") for b in sorted_betweenness[:20]: print(b) #First get the top 20 nodes by betweenness as a list top_betweenness = sorted_betweenness[:20] #Then find and print their degree for tb in top_betweenness: # Loop through top_betweenness (or eigenvector) degree = degree_dict[tb[0]] # Use degree_dict to access a node's degree, see footnote 2 print("ID:", tb[0], "| Betweenness Centrality:", tb[1], "| Degree:", degree)

sorted_eigenvector = sorted(eigenvector_dict.items(), key=itemgetter(1), reverse=True) print("Top 20 nodes by eigenvector centrality:") for b in sorted_eigenvector[:20]: print(b) #First get the top 20 nodes by eigenvector as a list top_eigenvector = sorted_eigenvector[:20] #Then find and print their degree for tb in top_eigenvector: # Loop through top_eigenvector degree = degree_dict[tb[0]] # Use degree_dict to access a node's degree, see footnote 2 print("ID:", tb[0], "| Eigenvector Centrality:", tb[1], "| Degree:", degree)

communities = community.greedy_modularity_communities(G)#模块化！！！ modularity_dict = {} # Create a blank dictionary for i,c in enumerate(communities): # Loop through the list of communities, keeping track of the number for the community for name in c: # Loop through each person in a community modularity_dict[name] = i # Create an entry in the dictionary for the person, where the value is which group they belong to. # Now you can add modularity information like we did the other metrics nx.set_node_attributes(G, modularity_dict, 'modularity') # First get a list of just the nodes in that class class0 = [n for n in G.nodes() if G.nodes[n]['modularity'] == 0] # Then create a dictionary of the eigenvector centralities of those nodes class0_eigenvector = {n:G.nodes[n]['eigenvector'] for n in class0} # Then sort that dictionary and print the first 5 results class0_sorted_by_eigenvector = sorted(class0_eigenvector.items(), key=itemgetter(1), reverse=True) print("Modularity Class 0 Sorted by Eigenvector Centrality:") for node in class0_sorted_by_eigenvector[:5]: print("Name:", node[0], "| Eigenvector Centrality:", node[1])

for i,c in enumerate(communities): # Loop through the list of communities if len(c) > 1: # Filter out modularity classes with 2 or fewer nodes print('Class '+str(i)+':', list(c)) # Print out the classes and their members

nx.write_gexf(G, 'shicai_network.gexf')