57 lines
2.3 KiB
Python
57 lines
2.3 KiB
Python
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import argparse
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import dgl
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import torch
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from ogb.nodeproppred import DglNodePropPredDataset
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from torch.utils.data import DataLoader
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from tqdm import tqdm
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from gnnrec.config import DATA_DIR
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from gnnrec.hge.utils import add_reverse_edges
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def random_walk(g, metapaths, num_walks, walk_length, output_file):
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"""在异构图上按指定的元路径随机游走，将轨迹保存到指定文件
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:param g: DGLGraph 异构图
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:param metapaths: Dict[str, List[str]] 起点类型到元路径的映射，元路径表示为边类型列表，起点和终点类型应该相同
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:param num_walks: int 每个顶点的游走次数
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:param walk_length: int 元路径重复次数
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:param output_file: str 输出文件名
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:return:
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"""
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with open(output_file, 'w') as f:
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for ntype, metapath in metapaths.items():
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print(ntype)
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loader = DataLoader(torch.arange(g.num_nodes(ntype)), batch_size=200)
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for b in tqdm(loader):
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nodes = torch.repeat_interleave(b, num_walks)
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traces, types = dgl.sampling.random_walk(g, nodes, metapath=metapath * walk_length)
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f.writelines([trace2name(g, trace, types) + '\n' for trace in traces])
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def trace2name(g, trace, types):
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return ' '.join(g.ntypes[t] + '_' + str(int(n)) for n, t in zip(trace, types) if int(n) >= 0)
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def main():
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parser = argparse.ArgumentParser(description='ogbn-mag数据集 metapath2vec基于元路径的随机游走')
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parser.add_argument('--num-walks', type=int, default=5, help='每个顶点游走次数')
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parser.add_argument('--walk-length', type=int, default=16, help='元路径重复次数')
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parser.add_argument('output_file', help='输出文件名')
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args = parser.parse_args()
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data = DglNodePropPredDataset('ogbn-mag', DATA_DIR)
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g = add_reverse_edges(data[0][0])
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metapaths = {
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'author': ['writes', 'has_topic', 'has_topic_rev', 'writes_rev'], # APFPA
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'paper': ['writes_rev', 'writes', 'has_topic', 'has_topic_rev'], # PAPFP
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'field_of_study': ['has_topic_rev', 'writes_rev', 'writes', 'has_topic'], # FPAPF
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'institution': ['affiliated_with_rev', 'writes', 'writes_rev', 'affiliated_with'] # IAPAI
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}
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random_walk(g, metapaths, args.num_walks, args.walk_length, args.output_file)
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if __name__ == '__main__':
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main()
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