使用DGL进行异构图元路径采样
异构图元路径采样
# -*- coding: utf-8 -*-
import dgl
import tqdm
import os
import multiprocessingnum_workers = 4def construct_graph():node_src = [1,2,0,3,4,5,6,7]node_dst = [2,0,1,1,1,6,7,8]data1 = (node_src,node_dst)data2 = (node_dst,node_src)hg = dgl.heterograph({('paper','pa','author'):data1,('author','ap','paper'):data2})return hgdef walk(args):G,walk_length, start_node, schema,num_walks_per_node = argstraces, _ = dgl.sampling.random_walk(G, [start_node] * num_walks_per_node, metapath=schema * walk_length)return traces#"paper - Author - paper " metapath sampling
def generate_metapath():path = '../output'output_path = open(os.path.join(path, "output_path_pb.txt"), "w")schema = ['pa' if i% 2==0 else 'ap' for i in range(4)]num_process = 4num_walks_per_node = 5walk_length = 10hg = construct_graph()index_paper_map = {1:'a',2:'b',0:'c',3:'d',4:'e',5:'f',6:'g',7:'h'}index_author_map = {2:'i',0:'j',1:'k',6:'l',7:'m',8:'n'}with multiprocessing.Pool(processes=num_process) as pool:iter = pool.imap(walk, ((hg,walk_length, node, schema,num_walks_per_node) for node in tqdm.trange(hg.number_of_nodes('paper'))),chunksize=128)#iter 中包含了num(authors)*num_walks_per_node条路径for idx,traces in enumerate(iter):for tr in traces:result = parse_trace(tr,index_paper_map,index_author_map)output_path.write(result+'\n')output_path.close()# for paper_id in tqdm.trange(hg.number_of_nodes('paper')):## #采样num_walks_per_node条路径# traces, _ = dgl.sampling.random_walk(# hg, [paper_id] * num_walks_per_node, metapath=schema*walk_length)# #写入文件# for tr in traces:# result = parse_trace(tr,index_paper_map,index_author_map)# output_path.write(result+'\n')# output_path.close()def parse_trace(trace, index_paper_map, index_author_map):s = []trace = trace.numpy()for index in range(trace.shape[0]):if index % 2 == 0: #papers.append(index_paper_map[trace[index]])else: #authors.append(index_author_map[trace[index]])return ','.join(s)if __name__ == '__main__':generate_metapath()
如果按照 论文-作者-论文（PAP)方式采样，得到以下结果
source code
使用DGL进行异构图元路径采样
异构图元路径采样
# -*- coding: utf-8 -*-
import dgl
import tqdm
import os
import multiprocessingnum_workers = 4def construct_graph():node_src = [1,2,0,3,4,5,6,7]node_dst = [2,0,1,1,1,6,7,8]data1 = (node_src,node_dst)data2 = (node_dst,node_src)hg = dgl.heterograph({('paper','pa','author'):data1,('author','ap','paper'):data2})return hgdef walk(args):G,walk_length, start_node, schema,num_walks_per_node = argstraces, _ = dgl.sampling.random_walk(G, [start_node] * num_walks_per_node, metapath=schema * walk_length)return traces#"paper - Author - paper " metapath sampling
def generate_metapath():path = '../output'output_path = open(os.path.join(path, "output_path_pb.txt"), "w")schema = ['pa' if i% 2==0 else 'ap' for i in range(4)]num_process = 4num_walks_per_node = 5walk_length = 10hg = construct_graph()index_paper_map = {1:'a',2:'b',0:'c',3:'d',4:'e',5:'f',6:'g',7:'h'}index_author_map = {2:'i',0:'j',1:'k',6:'l',7:'m',8:'n'}with multiprocessing.Pool(processes=num_process) as pool:iter = pool.imap(walk, ((hg,walk_length, node, schema,num_walks_per_node) for node in tqdm.trange(hg.number_of_nodes('paper'))),chunksize=128)#iter 中包含了num(authors)*num_walks_per_node条路径for idx,traces in enumerate(iter):for tr in traces:result = parse_trace(tr,index_paper_map,index_author_map)output_path.write(result+'\n')output_path.close()# for paper_id in tqdm.trange(hg.number_of_nodes('paper')):## #采样num_walks_per_node条路径# traces, _ = dgl.sampling.random_walk(# hg, [paper_id] * num_walks_per_node, metapath=schema*walk_length)# #写入文件# for tr in traces:# result = parse_trace(tr,index_paper_map,index_author_map)# output_path.write(result+'\n')# output_path.close()def parse_trace(trace, index_paper_map, index_author_map):s = []trace = trace.numpy()for index in range(trace.shape[0]):if index % 2 == 0: #papers.append(index_paper_map[trace[index]])else: #authors.append(index_author_map[trace[index]])return ','.join(s)if __name__ == '__main__':generate_metapath()
如果按照 论文-作者-论文（PAP)方式采样，得到以下结果
source code