add MTTM

examples/MTTM/Datasets/DataProcessing.py

@@ -0,0 +1,40 @@
+import networkx as nx
+import numpy as np
+import pandas as pd
+from node2vec import Node2Vec
+from node2vec.edges import HadamardEmbedder
+
+
+def get_graph(filepath):
+    dataGraph = pd.read_csv(filepath, sep=' ', skiprows=0)
+    graph = nx.Graph()
+    edges = np.array(dataGraph)
+    for edge in edges:
+        print('leftNode:', edge[0], 'rightNode:', edge[1])
+        graph.add_edge(edge[0], edge[1])
+    return graph
+
+
+def get_edge_embeddings(graph, savepath):  # node2vec生成边向量表征(64维)
+    node2vec = Node2Vec(graph, dimensions=64, walk_length=5, num_walks=10,
+                        workers=1)
+    model = node2vec.fit(window=10, min_count=1, batch_words=1)
+    edges_embs = HadamardEmbedder(keyed_vectors=model.wv)
+    edges_kv = edges_embs.as_keyed_vectors()
+    edges_kv.save_word2vec_format(savepath)
+
+
+def pro_data_main(dataset):
+    print("Input dataset:", dataset)
+    pathReal = './' + dataset + '/realData.csv'
+    #only real graph to be embedded
+    #pathFake = 'Data/' + dataset + '/fakeData.csv'
+    graph = get_graph(pathReal)  #real graph
+    savepath = './node2vecFeature/' + dataset.lower() + 'Feature.txt'  # 最终数据
+    get_edge_embeddings(graph, savepath)
+
+
+if __name__ == '__main__':
+    datasets = ['Facebook']
+    for dataset in datasets:
+        pro_data_main(dataset)