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main_node_attr_change.py
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main_node_attr_change.py
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import numpy as np
import scipy.sparse as sp
import utils
from hyperparameters import ALPHA, EPSILON, TRACKING_METHOD, LOAD_INSTEAD_OF_RECALCULATION, LOAD_MAX, DATASET_NAME
if DATASET_NAME == 'movielens-1m':
from dataprocessing_temporal_movielens import is_in_subgraph, match_from_sub_to_whole, match_from_whole_to_sub
elif DATASET_NAME == 'bitcoinalpha':
from dataprocessing_temporal_bitcoinalpha import is_in_subgraph, match_from_sub_to_whole, match_from_whole_to_sub
elif DATASET_NAME == 'wikilens':
from dataprocessing_temporal_wikilens import is_in_subgraph, match_from_sub_to_whole, match_from_whole_to_sub
import time
import pdb
HAVE_NODE_ATTR = 1
HAVE_EDGE_ATTR = 1
CHANGE_TYPE = 'node_attr_change'
def get_graph_info(graph_file_name, node_attr_file_name):
node_attr_set = set()
edge_attr_set = set()
graph_file = open(graph_file_name)
node_attr_file = open(node_attr_file_name)
for line in graph_file.readlines():
items = line.strip().split(',')
edge_attr = int(items[2])
edge_attr_set.add(edge_attr)
for line in node_attr_file.readlines():
items = line.strip().split(',')
node_attr = int(items[1])
node_attr_set.add(node_attr)
graph_file.close()
node_attr_file.close()
node_attr_list = []
edge_attr_list = []
for node_attr in node_attr_set:
node_attr_list.append(node_attr)
for edge_attr in edge_attr_set:
edge_attr_list.append(edge_attr)
return len(node_attr_set), len(edge_attr_set), node_attr_list, edge_attr_list
def build_matrices_accelerated(graph_file_name, node_attr_file_name, num_node_attr):
graph_file = graph_file_name
nodes_set = set()
node_attr_file_open = open(node_attr_file_name, 'r')
# first going through to count number of nodes
for line in node_attr_file_open.readlines():
items = line.strip().split(',')
node = int(items[0])
nodes_set.add(node)
nnodes = len(nodes_set)
adj_matrix = sp.lil_matrix((nnodes, nnodes))
node_attr_matrix = sp.lil_matrix((nnodes, nnodes))
node_attr_matrix_acce = sp.lil_matrix((nnodes, num_node_attr))
edge_attr_matrix = sp.lil_matrix((nnodes, nnodes))
node_attr_file_open.close()
# second going through to set up the adjacency (and possibly edge attribute) matrix
graph_file_open = open(graph_file, 'r')
for line in graph_file_open.readlines():
items = line.strip().split(',')
node_0 = int(items[0])
node_1 = int(items[1])
adj_matrix[node_0, node_1] = 1
adj_matrix[node_1, node_0] = 1
if (HAVE_EDGE_ATTR != 0):
edge_attr_matrix[node_0, node_1] = int(items[2])
edge_attr_matrix[node_1, node_0] = int(items[2])
graph_file_open.close()
# set up the node attribute matrix using a seperated file
if (HAVE_NODE_ATTR != 0):
node_attr_file_open = open(node_attr_file_name, 'r')
for line in node_attr_file_open.readlines():
items = line.strip().split(',')
node = int(items[0])
attr_of_node = int(items[1])
node_attr_matrix_acce[node, attr_of_node] = 1
node_attr_matrix[node, node] = attr_of_node
return nnodes, adj_matrix, node_attr_matrix, edge_attr_matrix, node_attr_matrix_acce
if __name__ == '__main__':
start_time = time.time()
num_node_attr, num_edge_attr, node_attr_list, edge_attr_list = get_graph_info('datasets/' + DATASET_NAME + '-temporal/edge.txt', 'datasets/' + DATASET_NAME + '-temporal/node_attr.txt')
nnodes_1, adj_matrix_1, node_attr_matrix_1, edge_attr_matrix_1, node_attr_matrix_acce_1 = build_matrices_accelerated(
'datasets/' + DATASET_NAME + '-temporal/edge_sorted_sub.txt', 'datasets/' + DATASET_NAME + '-temporal/node_attr_sub.txt', max(node_attr_list) + 1
)
nnodes_2, adj_matrix_2, node_attr_matrix_2, edge_attr_matrix_2, node_attr_matrix_acce_2 = build_matrices_accelerated(
'datasets/' + DATASET_NAME + '-temporal/edge_sorted.txt', 'datasets/' + DATASET_NAME + '-temporal/node_attr.txt', max(node_attr_list) + 1
)
node_change_file = open('datasets/' + DATASET_NAME + '-temporal/' + CHANGE_TYPE + '.txt', 'r')
all_line = node_change_file.readlines()
node_change_file.close()
# data structure for pagerank vector updating
P_dict = {}
M = {}
q = {}
v = {}
time_record = []
# data structure for filtering
all_nodes_in_whole = set()
for node in range(nnodes_2):
all_nodes_in_whole.add(node)
candidate = {node: all_nodes_in_whole.copy() for node in range(nnodes_1)}
F = {} # F((j, i)) will be used to record which (NodeAttribute, EdgeAttribute) filtered node j in G_2 away from candidate[i]
# get the ground truth match
ground_truth_file = open('datasets/' + DATASET_NAME + '-temporal/ground_truth.txt', 'r')
ground_truth = {}
for line in ground_truth_file.readlines():
items = line.strip().split(',')
node_in_whole = int(items[0])
node_in_sub = int(items[1])
ground_truth[node_in_sub] = node_in_whole
# pre-computing
if not LOAD_INSTEAD_OF_RECALCULATION:
# transition matrix P
P = utils.calculate_transition_matrix(node_attr_list, edge_attr_list, nnodes_1, adj_matrix_1, node_attr_matrix_1, edge_attr_matrix_1, node_attr_matrix_acce_1, nnodes_2, adj_matrix_2, node_attr_matrix_2, edge_attr_matrix_2, node_attr_matrix_acce_2)
# pre-knowledge h
h = utils.calculate_pre_knowledge_h(nnodes_1, nnodes_2, candidate, adj_matrix_1, node_attr_matrix_1, edge_attr_matrix_1, adj_matrix_2, node_attr_matrix_2, edge_attr_matrix_2, F)
sp.save_npz('./' + CHANGE_TYPE + '_saved_data/' + DATASET_NAME + '/P_matrix_start.npz', P)
np.save('./' + CHANGE_TYPE + '_saved_data/' + DATASET_NAME + '/h_array_start.npy', h)
else:
P = sp.load_npz('./' + CHANGE_TYPE + '_saved_data/' + DATASET_NAME + '/P_matrix_start.npz')
h = np.load('./' + CHANGE_TYPE + '_saved_data/' + DATASET_NAME + '/h_array_start.npy')
P_dict[0] = P
# the one-hot solution
if (TRACKING_METHOD == 3):
P = P.tocsr()
number = 0
onehot_ppr = utils.calc_onehot_ppr_matrix(P, ALPHA, 2).tolil().transpose()
for i in range(nnodes_1 * nnodes_2):
q[i] = onehot_ppr[i, :]
M[i] = P_dict[0]
if (number % 100000) == 0:
print(number)
number += 1
pre_end_time = time.time()
print("data processing and precomputing time:", pre_end_time - start_time)
# initial pagerank vector
v[0] = utils.calc_ppr_by_power_iteration(P, ALPHA, h, 20)
exp_match = utils.greedy_match(v[0], nnodes_1, nnodes_2)
utils.check_greedy_hit1(exp_match, ground_truth, nnodes_1)
counter = 0
for line in all_line:
items = line.strip().split(',')
change_node_in_sub = int(items[0])
change_from = node_attr_matrix_1[change_node_in_sub, change_node_in_sub]
change_to = int(items[1])
counter += 1
node_attr_matrix_1[change_node_in_sub, change_node_in_sub] = change_to
node_attr_matrix_2[match_from_sub_to_whole(change_node_in_sub), match_from_sub_to_whole(change_node_in_sub)] = change_to
node_attr_matrix_acce_1[change_node_in_sub, change_from] = 0
node_attr_matrix_acce_1[change_node_in_sub, change_to] = 1
node_attr_matrix_acce_2[match_from_sub_to_whole(change_node_in_sub), change_from] = 0
node_attr_matrix_acce_2[match_from_sub_to_whole(change_node_in_sub), change_to] = 1
print('change time: ', counter)
if LOAD_INSTEAD_OF_RECALCULATION and (counter <= LOAD_MAX):
P_new = sp.load_npz('./' + CHANGE_TYPE + '_saved_data/' + DATASET_NAME + '/P_matrix_' + str(counter) + '.npz')
h_new_sp = sp.load_npz('./' + CHANGE_TYPE + '_saved_data/' + DATASET_NAME + '/h_array_sp_' + str(counter) + '.npz')
h_new = h_new_sp.toarray().ravel()
else:
P_new = utils.calculate_transition_matrix(node_attr_list, edge_attr_list, nnodes_1, adj_matrix_1, node_attr_matrix_1, edge_attr_matrix_1, node_attr_matrix_acce_1, nnodes_2, adj_matrix_2, node_attr_matrix_2, edge_attr_matrix_2, node_attr_matrix_acce_2)
h_new = utils.calculate_pre_knowledge_h(nnodes_1, nnodes_2, candidate, adj_matrix_1, node_attr_matrix_1, edge_attr_matrix_1, adj_matrix_2, node_attr_matrix_2, edge_attr_matrix_2, F)
sp.save_npz('./' + CHANGE_TYPE + '_saved_data/' + DATASET_NAME + '/P_matrix_' + str(counter) + '.npz', P_new)
h_new_sp = sp.csr_matrix(h_new)
sp.save_npz('./' + CHANGE_TYPE + '_saved_data/' + DATASET_NAME + '/h_array_sp_' + str(counter) + '.npz', h_new_sp)
time1 = time.time()
if TRACKING_METHOD == 2:
v[counter] = utils.tracking_method_two(v[counter-1], P, P_new, h_new, ALPHA, EPSILON)
else: # TRACKING_METHOD == 3
P_dict[counter] = P_new.copy()
v_mid = utils.osp(v[counter-1], P, P_new, ALPHA, EPSILON, 1)
delta_h = h_new - h
print("number of changed elements in h:", np.count_nonzero(delta_h))
for i in range(nnodes_1 * nnodes_2):
if delta_h[i] != 0:
q_new = utils.osp(q[i].toarray().ravel(), M[i], P_new, ALPHA, EPSILON, 0)
q[i] = sp.lil_matrix(q_new)
M[i] = P_dict[counter]
v_mid = v_mid + delta_h[i] * q_new
v[counter] = v_mid
time2 = time.time()
print("tracking time: ", time2 - time1)
time_record.append(time2 - time1)
P = P_new.copy()
h = h_new.copy()
exp_match = utils.greedy_match(v[counter], nnodes_1, nnodes_2)
utils.check_greedy_hit1(exp_match, ground_truth, nnodes_1)
pdb.set_trace()