def get_context_pairs(graphs, num_time_steps): """ Load/generate context pairs for each snapshot through random walk sampling.""" load_path = "data/{}/train_pairs_n2v_{}.pkl".format(FLAGS.dataset, str(num_time_steps - 2)) try: context_pairs_train = dill.load(open(load_path, 'rb')) print("Loaded context pairs from pkl file directly") except (IOError, EOFError): print("Computing training pairs ...") context_pairs_train = [] for i in range(0, num_time_steps): context_pairs_train.append(run_random_walks_n2v(graphs[i], graphs[i].nodes())) dill.dump(context_pairs_train, open(load_path, 'wb')) print ("Saved pairs") return context_pairs_train
def get_context_pairs(graphs, num_time_steps): """ Load/generate context pairs for each snapshot through random walk sampling.""" load_path = "data/{}/train_pairs_n2v_{}.pkl".format( FLAGS.dataset, str(num_time_steps - 2)) try: # dill 可以用于保存对象等大多数Python的数据格式 context_pairs_train = dill.load(open(load_path, 'rb')) print("Loaded context pairs from pkl file directly") except (IOError, EOFError): print("Computing training pairs ...") context_pairs_train = [] #num_time_steps表示训练的静态快照图, #所以这里的context_pairs_train列表包含num_time_steps个数的字典。 for i in range(0, num_time_steps): #run_random_walks_n2v()返回的是一个字典。 context_pairs_train.append( run_random_walks_n2v(graphs[i], graphs[i].nodes())) #保存不同时刻图的随机游走的固定窗口的节点对。 dill.dump(context_pairs_train, open(load_path, 'wb')) print("Saved pairs") return context_pairs_train
def get_context_pairs_incremental(graph): return run_random_walks_n2v(graph, graph.nodes())