reorganize

1 files changed, 81 insertions, 0 deletions

diff --git a/code/drawGraph.py b/code/drawGraph.py
new file mode 100644
index 0000000..5650145
--- /dev/null
+++ b/code/drawGraph.py
@@ -0,0 +1,81 @@
+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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