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import networkx as nx
import csv
import pandas as pd
import dgl
def read_graph_networkx(node_file,edge_file):
G = nx.Graph()
node_list=[]
edge_list=[]
with open(node_file, 'r', encoding="utf-8") as csvfile:
nodes = csv.DictReader(csvfile)
for node in nodes:
node_list.append((node["index"],{"label":node["name"]}))
G.add_nodes_from(node_list)
with open(edge_file, 'r', encoding="utf-8") as edgefile:
reader1 = csv.reader(edgefile)
edges = [" ".join(row) for row in reader1]
edge_count = pd.value_counts(edges).to_dict()
for key in edge_count:
edge_param=key.split(" ")
edge_list.append((edge_param[0],edge_param[1]))
G.add_edges_from(edge_list)
print(G)
# nx.draw(G,font_size=20)
# plt.show()
subgraphs=nx.connected_components(G)
for c in sorted(subgraphs,key=len,reverse=True):
if len(c)<=10:
break
print(G.subgraph(c))
# nx.draw(largest_connected_subgraph,with_labels=True)
def read_graph_dgl(node_file,edge_file_fraud,edge_file_legi):
graph_dict={}
node_dict={}
with open(node_file, 'r', encoding="utf-8") as nodefile:
nodes = csv.DictReader(nodefile)
for node in nodes:
if node["type"] == '0':
node_dict[node["index"]]="sender_domain"
elif node["type"] == '1':
node_dict[node["index"]]="inter_domain"
elif node["type"] == '2':
node_dict[node["index"]]="IP"
else:
node_dict[node["index"]]="client"
with open(edge_file_fraud, 'r', encoding="utf-8") as edgefile:
reader1 = csv.reader(edgefile)
edges = [" ".join(row) for row in reader1]
edge_count = pd.value_counts(edges).to_dict()
for key in edge_count:
edge_param=key.split(" ")
if graph_dict[(node_dict[edge_param[0]],"fraud",node_dict[edge_param[1]])]:
graph_dict[(node_dict[edge_param[0]], "fraud", node_dict[edge_param[1]])].append((edge_param[0],edge_param[1]))
else:
graph_dict[(node_dict[edge_param[0]], "fraud", node_dict[edge_param[1]])]=[(edge_param[0],edge_param[1])]
# 字典的每个值都是一个元组的列表。
# 节点是从零开始的整数ID。 不同类型的节点ID具有单独的计数。
ratings = dgl.heterograph(graph_dict)
import torch as th
def test():
# 边 0->1, 0->2, 0->3, 1->3
u, v = th.tensor(["qq.com","113.108.11.234","qq.com"]), th.tensor(["qq.com", "qq.com","127.0.0.1"])
g = dgl.graph((u, v))
print(g) # 图中节点的数量是DGL通过给定的图的边列表中最大的点ID推断所得出的
# 获取节点的ID
print(g.nodes())
# 获取边的对应端点
print(g.edges())
# 获取边的对应端点和边ID
print(g.edges(form='all'))
if __name__=="__main__":
# read_graph_dgl("all_nodes.csv","fraud_edges_index_only.csv","")
# read_graph_networkx("all_nodes.csv","all_edges_index_only.csv")
test()
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