def exec_struc2vec(args): ''' Pipeline for representational learning for all nodes in a graph. ''' if (args.OPT3): until_layer = args.until_layer else: until_layer = None G = read_graph() G = struc2vec.Graph(G, args.directed, args.workers, untilLayer=until_layer) if (args.OPT1): G.preprocess_neighbors_with_bfs_compact() else: G.preprocess_neighbors_with_bfs() if (args.OPT2): G.create_vectors() G.calc_distances(compactDegree=args.OPT1) else: G.calc_distances_all_vertices(compactDegree=args.OPT1) G.create_distances_network() G.preprocess_parameters_random_walk() G.simulate_walks(args.num_walks, args.walk_length) return G
def struc2vec(self): if self.nodeGraph == '': return beginTime = time.time() print('1: struc2vec Begin') strucGraph = graph.from_networkx(self.nodeGraph) strucGraph = struc2vec.Graph(strucGraph, 'undirected', 4, untilLayer=None) if True: strucGraph.preprocess_neighbors_with_bfs_compact() else: strucGraph.preprocess_neighbors_with_bfs() if True: strucGraph.create_vectors() strucGraph.calc_distances(compactDegree=True) else: strucGraph.calc_distances_all_vertices(compactDegree=True) strucGraph.create_distances_network() strucGraph.preprocess_parameters_random_walk() strucGraph.simulate_walks(10, 80) walks = LineSentence('random_walks.txt') self.model = Word2Vec(walks, size=self.D, window=10, min_count=0, hs=1, sg=1, workers=4, iter=5) print('Time of Struc2Vec', time.time() - beginTime)
def exec_struc2vec(input_file): ''' Pipeline for representational learning for all nodes in a graph. ''' until_layer = 6 G = read_graph(input_file) G = struc2vec.Graph(G, 'undirected', workers=4, untilLayer=until_layer) G.preprocess_neighbors_with_bfs_compact() G.create_vectors() G.calc_distances(compactDegree=True) G.create_distances_network() G.preprocess_parameters_random_walk() G.simulate_walks(10, 80) return G
def exec_struc2vec(args): ''' Pipeline for representational learning for all nodes in a graph. ''' #K = args.num_walks #S = args.walk_length K = 10 S = 5 G = read_graph() G = struc2vec.Graph(G, args.directed, args.workers, K, S) G.preprocess_neighbors_with_rw() G.create_vectors() G.calc_distances() G.create_distances_network() walks = G.simulate_walks(args.num_walks, args.walk_length) return walks