wehub-resource-sync
114 lines
3.2 KiB
Python
114 lines
3.2 KiB
Python
import os
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import random
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import sys
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import time
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import dgl
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import numpy as np
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import tqdm
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num_walks_per_node = 1000
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walk_length = 100
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path = sys.argv[1]
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def construct_graph():
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paper_ids = []
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paper_names = []
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author_ids = []
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author_names = []
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conf_ids = []
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conf_names = []
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f_3 = open(os.path.join(path, "id_author.txt"), encoding="ISO-8859-1")
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f_4 = open(os.path.join(path, "id_conf.txt"), encoding="ISO-8859-1")
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f_5 = open(os.path.join(path, "paper.txt"), encoding="ISO-8859-1")
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while True:
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z = f_3.readline()
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if not z:
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break
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z = z.strip().split()
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identity = int(z[0])
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author_ids.append(identity)
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author_names.append(z[1])
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while True:
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w = f_4.readline()
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if not w:
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break
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w = w.strip().split()
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identity = int(w[0])
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conf_ids.append(identity)
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conf_names.append(w[1])
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while True:
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v = f_5.readline()
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if not v:
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break
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v = v.strip().split()
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identity = int(v[0])
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paper_name = "p" + "".join(v[1:])
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paper_ids.append(identity)
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paper_names.append(paper_name)
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f_3.close()
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f_4.close()
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f_5.close()
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author_ids_invmap = {x: i for i, x in enumerate(author_ids)}
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conf_ids_invmap = {x: i for i, x in enumerate(conf_ids)}
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paper_ids_invmap = {x: i for i, x in enumerate(paper_ids)}
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paper_author_src = []
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paper_author_dst = []
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paper_conf_src = []
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paper_conf_dst = []
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f_1 = open(os.path.join(path, "paper_author.txt"), "r")
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f_2 = open(os.path.join(path, "paper_conf.txt"), "r")
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for x in f_1:
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x = x.split("\t")
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x[0] = int(x[0])
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x[1] = int(x[1].strip("\n"))
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paper_author_src.append(paper_ids_invmap[x[0]])
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paper_author_dst.append(author_ids_invmap[x[1]])
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for y in f_2:
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y = y.split("\t")
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y[0] = int(y[0])
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y[1] = int(y[1].strip("\n"))
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paper_conf_src.append(paper_ids_invmap[y[0]])
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paper_conf_dst.append(conf_ids_invmap[y[1]])
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f_1.close()
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f_2.close()
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hg = dgl.heterograph(
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{
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("paper", "pa", "author"): (paper_author_src, paper_author_dst),
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("author", "ap", "paper"): (paper_author_dst, paper_author_src),
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("paper", "pc", "conf"): (paper_conf_src, paper_conf_dst),
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("conf", "cp", "paper"): (paper_conf_dst, paper_conf_src),
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}
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)
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return hg, author_names, conf_names, paper_names
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# "conference - paper - Author - paper - conference" metapath sampling
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def generate_metapath():
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output_path = open(os.path.join(path, "output_path.txt"), "w")
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count = 0
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hg, author_names, conf_names, paper_names = construct_graph()
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for conf_idx in tqdm.trange(hg.num_nodes("conf")):
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traces, _ = dgl.sampling.random_walk(
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hg,
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[conf_idx] * num_walks_per_node,
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metapath=["cp", "pa", "ap", "pc"] * walk_length,
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)
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for tr in traces:
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outline = " ".join(
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(conf_names if i % 4 == 0 else author_names)[tr[i]]
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for i in range(0, len(tr), 2)
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) # skip paper
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print(outline, file=output_path)
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output_path.close()
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if __name__ == "__main__":
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generate_metapath()
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