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pre-offline.py
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import networkx as nx
import os
import matplotlib.pyplot as plt
import random
import numpy as np
import time
from Tools.calculate import compute_distance, compute_support
from Tools.aid import create_folder
from Tools.inf_score_u_v import max_weight_path, max_weight_product_path
from Tools.collapse_calculate import collapse_calculate
rand_seed = 2023
random.seed(rand_seed)
np.random.seed(rand_seed)
def get_dataset(dataset, path, keywords_num, keywords_pre_need_num, R_MAX, N_MAX, number_select_pivots):
G = nx.Graph()
Graph_dataset = open(path, "r")
lines = Graph_dataset.readlines()
for lines in lines:
list = lines.split()
node1, node2 = int(list[0]), int(list[1])
G.add_edge(node1, node2)
# print(list, type(list))
graphData_build(G=G,
keywords_num=keywords_num,
keywords_pre_need_num=keywords_pre_need_num,
dataset=dataset,
n=number_select_pivots)
for node_index in G.nodes:
# print(type(node_index))
compute_bv_and_ub_sup(node_index=node_index, R_MAX=R_MAX, N_MAX=N_MAX, G=G)
compute_range(node_index=node_index, G=G, R_MAX=R_MAX)
folder_name = os.path.join(
"Out",
"pre-compute",
dataset,
"{}-{}-{}-{}".format(
G.number_of_nodes(),
G.number_of_edges(),
keywords_num,
keywords_pre_need_num
)
)
create_folder(folder_name)
initial_directory = os.getcwd()
os.chdir(folder_name)
nx.write_gml(G, 'G.gml')
print(folder_name, 'G.gml', 'saved successfully!')
os.chdir(initial_directory)
def graphData_build(G: nx.classes.graph.Graph, keywords_num: int, keywords_pre_need_num: int, dataset: str, n: int):
# print("nodes number: {}".format(G.number_of_nodes()))
# print("edges number: {}".format(G.number_of_edges()))
# print(nx.info(G))
print(G)
# print(type(G))
# print(nx.degree(G))
# print(G.number_of_nodes())
average_degree = sum(d for v, d in nx.degree(G)) / G.number_of_nodes()
print("Average degree: {}".format(average_degree))
print("Max degree: {}".format(max(d for v, d in nx.degree(G))))
print("Min degree: {}".format(min(d for v, d in nx.degree(G))))
# TODO: add keywords to vertices
# the type of node_index is str
keywords_set = range(0, keywords_num)
label_cnt = [0] * keywords_num
for i in G.nodes:
# every vertex keywords size in (keywords_pre_need_num-1, keywords_pre_need_num+1)
keywords_need_num = np.random.randint(max(keywords_pre_need_num-1, 1), keywords_pre_need_num+2)
keywords = random.sample(keywords_set, keywords_need_num)
G.nodes[i]['keywords'] = keywords
for keyword in keywords:
label_cnt[keyword] += 1
# print(G.nodes[i])
# print(label_cnt)
# TODO: vit vector(bin)
for node in G.nodes:
bv = 0
for keyword in G.nodes[node]["keywords"]:
bv |= (1 << keyword)
# print(G_data.nodes[node_index]['keywords'])
# print(bin(bv))
G.nodes[node]["BV"] = bv
# print(type(G.nodes[node]["BV"]))
# TODO: add weight to edges
for i in G.nodes:
for neighbor in G.neighbors(i):
G.edges[i, neighbor]['weight'] = random.uniform(0.5, 0.6)
# TODO: add distance to nodes
pivots = random.sample(G.nodes, n)
print("randomly select pivots: {}".format(pivots))
# TODO: update pivots by cost model
for node in G.nodes:
dist = [0] * n
for i in range(n):
dist[i] = compute_distance(G, node, pivots[i])
G.nodes[node]["dist"] = dist
folder_name = os.path.join(
"Out",
"pre-compute",
dataset,
"{}-{}-{}-{}".format(
G.number_of_nodes(),
G.number_of_edges(),
keywords_num,
keywords_pre_need_num
)
)
create_folder(folder_name)
initial_directory = os.getcwd()
os.chdir(folder_name)
nx.write_gml(G, 'G.gml')
print(folder_name, 'G.gml', 'saved successfully!')
os.chdir(initial_directory)
def compute_bv_and_ub_sup(node_index, R_MAX: int, N_MAX: int, G: nx.classes.graph.Graph):
G.nodes[node_index]["Aux"] = [{
"BV_r": 0,
"ub_sup_r": 0,
"ub_inf_r": 0
# "inf_r": [0] # community_1-hop boundary influence set
} for _ in range(R_MAX)]
# TODO: rmax-hop support compute
# R_MAX in [1-7]
r_hop_max = nx.ego_graph(G=G, n=node_index, radius=R_MAX, center=True)
# print(r_hop_max)
r_hop_max_support = compute_support(graph=r_hop_max)
# print(r_hop_max_support.edges[0, 1]["ub_sup"])
for (u, v) in r_hop_max_support.edges:
if "ub_sup" not in G.edges[u, v]:
G.edges[u, v]["ub_sup"] = 0
if G.edges[u, v]["ub_sup"] < r_hop_max_support.edges[u, v]["ub_sup"]:
G.edges[u, v]["ub_sup"] = r_hop_max_support.edges[u, v]["ub_sup"]
# print("[{},{}] sup is {}".format(u, v, G.edges[u, v]["ub_sup"]))
def compute_range(node_index, G: nx.classes.graph.Graph, R_MAX: int):
for r in range(R_MAX):
r_hop = nx.ego_graph(G=G, n=node_index, radius=r+1, center=True)
# print(r_hop)
for node_j in r_hop.nodes:
# print(type(G.nodes[node_index]["Aux"][r]["BV_r"]))
# print(type(r_hop.nodes[node_j]["BV"]))
# print(r_hop.nodes[node_j]["BV"])
G.nodes[node_index]["Aux"][r]["BV_r"] = G.nodes[node_index]["Aux"][r]["BV_r"] | int(r_hop.nodes[node_j]["BV"])
for (u, v) in r_hop.edges:
if u > v and r_hop.edges[u, v]["ub_sup"] > G.nodes[node_index]["Aux"][r]["ub_sup_r"]:
G.nodes[node_index]["Aux"][r]["ub_sup_r"] = r_hop.edges[u, v]["ub_sup"]
# TODO: ub_bound_influence
print("finish {} node".format(node_index))
if __name__ == '__main__':
dataset_Amazon = "Amazon"
dataset_DBLP = "DBLP"
dataset_eu = "Eu"
path_Amazon = "Dataset/Amazon/com-amazon.ungraph.txt"
path_DBLP = "Dataset/DBLP/com-dblp.ungraph.txt"
path_Eu = "Dataset/Eu-core/email-Eu-core.txt"
keywords_num = 50
keywords_pre_need_num = 3
R_MAX = 3
N_MAX = 100
number_select_pivots = 5
# get_dataset(dataset=dataset_Amazon,
# path=path_Amazon,
# keywords_num=keywords_num,
# keywords_pre_need_num=keywords_pre_need_num,
# R_MAX=R_MAX,
# N_MAX=N_MAX,
# number_select_pivots=number_select_pivots)
get_dataset(dataset=dataset_DBLP,
path=path_DBLP,
keywords_num=keywords_num,
keywords_pre_need_num=keywords_pre_need_num,
R_MAX=R_MAX,
N_MAX=N_MAX,
number_select_pivots=number_select_pivots)
# get_dataset(dataset=dataset_eu,
# path=path_Eu,
# keywords_num=keywords_num,
# keywords_pre_need_num=keywords_pre_need_num,
# R_MAX=R_MAX,
# N_MAX=N_MAX,
# number_select_pivots=number_select_pivots)
# dataset = "Facebook"
# Graph_dataset = open("Dataset/Facebook/facebook_combined.txt", "r")
# lines = Graph_dataset.readlines()
# # print(len(lines))
# for lines in lines:
# list = lines.split()
# node1, node2 = int(list[0]), int(list[1])
# G.add_edge(node1, node2)
# # print(list, type(list))
#
# start_time = time.time()
# graphData_build(G=G,
# keywords_num=keywords_num,
# keywords_pre_need_num=keywords_pre_need_num,
# dataset=dataset,
# n=number_select_pivots)
# print("build Graph base data cost time:{}".format(time.time() - start_time))
#
# G = nx.read_gml("Out/pre-compute/Facebook/4039-88234-50-3/G.gml")
# for node_index in G.nodes:
# # print(type(node_index))
# compute_bv_and_ub_sup(node_index=node_index, R_MAX=R_MAX, N_MAX=N_MAX, G=G)
# compute_range(node_index=node_index, G=G, R_MAX=R_MAX)
# folder_name = os.path.join(
# "Out",
# "pre-compute",
# dataset,
# "{}-{}-{}-{}".format(
# G.number_of_nodes(),
# G.number_of_edges(),
# keywords_num,
# keywords_pre_need_num
# )
# )
#
# create_folder(folder_name)
# initial_directory = os.getcwd()
# os.chdir(folder_name)
# nx.write_gml(G, 'G.gml')
# print(folder_name, 'G.gml', 'saved successfully!')
# os.chdir(initial_directory)
# print(G)
# print(G.nodes["1"]["dist"], G.nodes["1"]["keywords"], G.edges["1", "0"]["weight"])
# compute_bv_and_ub_sup_and_col(node_index=1, R_MAX=3, N_MAX=50, G=G)
# print("build offline data Aux cost time:{}".format(time.time() - start_time))
# test of compute influence inf_score(u, v)
# u = 1 # 101
# v = 267 # 2089
# start_time = time.time()
# max_weight, path = max_weight_path(graph=G, u=u, v=v)
# print(f"inf_score({u}, {v}): {max_weight}")
# print(f"inf_score_path({u}, {v}): {' -> '.join(map(str, path))}")
# print("compute influence inf_score({}, {}) cost time:{}".format(u, v, time.time() - start_time))
#
# start_time = time.time()
# max_product, max_product_path = max_weight_product_path(G, u, v)
# print(f"inf_score({u}, {v}): {max_product}")
# print(f"inf_score_path({u}, {v}): {' -> '.join(map(str, max_product_path))}")
# print("compute influence inf_score({}, {}) cost time:{}".format(u, v, time.time() - start_time))
# print(G.edges[1, 0]["weight"]*G.edges[0, 2]["weight"])
# print(G.edges[2, 0]["weight"] * G.edges[0, 1]["weight"])
# print(G.edges[101, 0]["weight"]
# * G.edges[0, 136]["weight"]
# * G.edges[136, 1912]["weight"]
# * G.edges[1912, 2089]["weight"])
# start_time = time.time()
# for node in G.nodes:
# compute_bv_and_ub_sup_and_col(node_index=node, R_MAX=3, N_MAX=50, G=G)
# print("build offline data Aux cost time:{}".format(time.time()-start_time))
# compute_bv_and_ub_sup(node_index=1, R_MAX=3, N_MAX=50, G=G)
# start_time = time.time()
# compute_range(node_index=1, G=G, R_MAX=3)
# print("test compute_range cost time:{}".format(time.time() - start_time))
# G = nx.read_gml("Out/pre-compute/Facebook/4039-88234-50-3/G.gml")
# print(G)
# for node in G.nodes:
# print(G.nodes[node]["BV"])
# print(G.nodes[node]["Aux"])