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degree_nsgt.py
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# -*- coding: utf-8 -*-
# @Author : qiaohezhe
# @github : https://github.com/fengduqianhe
# @Date : 8/31/2022
# version： Python 3.7.8
# @File : degree_nsgt.py
# @Software: PyCharm
# @Institution: SMU
import numpy as np
import scipy.io as sio
import scipy.sparse as sp
from collections import Counter
from sklearn.metrics import roc_auc_score
import scipy.io as scio
import matplotlib.pyplot as plt
from sklearn.metrics import precision_recall_curve, average_precision_score
def preprocess_features(features):
"""Row-normalize feature matrix and convert to tuple representation"""
rowsum = np.array(features.sum(1))
r_inv = np.power(rowsum, -1).flatten()
r_inv[np.isinf(r_inv)] = 0.
r_mat_inv = sp.diags(r_inv)
features = r_mat_inv.dot(features)
return features
np.random.seed(1)
dataset_name = 'Facebook'
data = sio.loadmat('./data/{}.mat'.format(dataset_name))
label = data['Label'] if ('Label' in data) else data['gnd']
attr = data['Attributes'] if ('Attributes' in data) else data['X']
network = data['Network'] if ('Network' in data) else data['A']
# label = data['label'] if ('label' in data) else data['gnd']
# attr = data['features'] if ('features' in data) else data['X']
# network = data['net_upu'] if ('net_upu' in data) else data['A']
if dataset_name in ['Amazon', 'YelpChi', 'Amazon-all', 'YelpChi-all']:
attr, _ = preprocess_features(attr)
attr_matrix = attr.toarray()
adj_matrix = network.toarray()
label = np.squeeze(np.array(label))
print(Counter(label))
def calc_degree(adj_matrix):
row = adj_matrix.shape[0]
col = adj_matrix.shape[1]
dis_array = np.zeros((row, col))
min_dis = 100
max_dis = 0
degree = np.sum(adj_matrix, axis=1)
for i in range(row):
print(i)
node_index = np.argwhere(adj_matrix[i, :] == 1)[:, 0]
for j in node_index:
dis = np.abs(degree[i] - degree[j])
dis_array[i][j] = dis
if dis > max_dis:
max_dis = dis_array[i][j]
if dis < min_dis:
min_dis = dis_array[i][j]
return dis_array, max_dis, min_dis
def get_cos_similar(v1: list, v2: list):
num = float(np.dot(v1, v2)) # 向量点乘
denom = np.linalg.norm(v1) * np.linalg.norm(v2) # 求模长的乘积
return 0.5 + 0.5 * (num / denom) if denom != 0 else 0
def calc_sim(adj_matrix, attr_matrix):
row = adj_matrix.shape[0]
col = adj_matrix.shape[1]
dis_array = np.zeros((row, col))
for i in range(row):
# print(i)
node_index = np.argwhere(adj_matrix[i, :] > 0)[:, 0]
for j in node_index:
dis = get_cos_similar(attr_matrix[i].tolist(), attr_matrix[j].tolist())
dis_array[i][j] = dis
return dis_array
def calc_dis(adj_matrix, attr_matrix):
row = adj_matrix.shape[0]
col = adj_matrix.shape[1]
dis_array = np.zeros((row, col))
min_dis = 100
max_dis = 0
for i in range(row):
print(i)
node_index = np.argwhere(adj_matrix[i, :] > 0)[:, 0]
for j in node_index:
dis = np.sqrt(np.sum((attr_matrix[i] - attr_matrix[j]) * (attr_matrix[i] - attr_matrix[j])))
dis_array[i][j] = dis
if dis > max_dis:
max_dis = dis_array[i][j]
if dis < min_dis:
min_dis = dis_array[i][j]
return dis_array, max_dis, min_dis
def graph_nsgt(adj_matrix, dis_array):
row = adj_matrix.shape[0]
new_adj_matrix = adj_matrix.copy()
# max_dis = dis_array.max()
# min_dis = dis_array[dis_array != 0].min()
dis_array_u = dis_array * adj_matrix
mean_dis = dis_array_u[dis_array_u != 0].mean()
for i in range(row):
node_index = np.argwhere(new_adj_matrix[i, :] > 0)
node_index = node_index.reshape(node_index.shape[0])
if node_index.shape[0] != 0:
max_dis = dis_array[i, node_index].max()
min_dis = mean_dis
# min_dis = dis_array[i, node_index].min()
if max_dis > min_dis:
random_value = (max_dis - min_dis) * np.random.random_sample() + min_dis
cutting_edge = np.argwhere(dis_array[i, node_index] > random_value)
cutting_edge = cutting_edge.reshape(cutting_edge.shape[0])
if cutting_edge.shape[0] != 0:
new_adj_matrix[i, node_index[cutting_edge]] = 0
new_adj_matrix = new_adj_matrix + new_adj_matrix.T
new_adj_matrix[new_adj_matrix > 1] = 1
return new_adj_matrix
# raw feature nsgt
dis_array, max_dis, min_dis = calc_dis(adj_matrix, attr_matrix)
origin_adj = adj_matrix
N_t = 10
for i in range(N_t):
new_adj_matrix = graph_nsgt(adj_matrix, dis_array)
# score1 = np.sum(origin_adj, 0)
# print(score1.tolist())
# print(np.sum(origin_adj, 0))
# print(np.sum(new_adj_matrix, 0))
score1 = (np.sum(origin_adj, 0) - np.sum(new_adj_matrix, 0)) / (np.sum(origin_adj, 0))
adj_matrix = new_adj_matrix
score1[np.isinf(score1)] = 0.
score1[np.isnan(score1)] = 0.
score = score1
auc = roc_auc_score(label, score)
# print(score.tolist())
print('AUC:{:.4f}'.format(auc))
AP = average_precision_score(label, score, average='macro', pos_label=1, sample_weight=None)
print('AP:', AP)
normal_node_index = np.where(label == 0)[0]
abnormal_node_index = np.where(label == 1)[0]
normal_node_degree = score[normal_node_index]
abnormal_node_degree = score[abnormal_node_index]
data = [normal_node_degree, abnormal_node_degree]
fig, ax = plt.subplots()
ax.boxplot(data)
# plt.ylim(0, 10)
ax.set_xticklabels(["normal", "abnormal"]) # 设置x轴刻度标签
plt.title('Node Degree')
plt.show()