| import seaborn as sns; sns.set() |
| import matplotlib.pyplot as plt |
| from tqdm import tqdm |
| import os |
| import numpy as np |
| import scipy.sparse as sp |
|
|
| def degree_dist(clean_adj, perturbed_adj, savename='degree_dist.pdf'): |
| """Plot degree distributnio on clean and perturbed graphs. |
| |
| Parameters |
| ---------- |
| clean_adj: sp.csr_matrix |
| adjancecy matrix of the clean graph |
| perturbed_adj: sp.csr_matrix |
| adjancecy matrix of the perturbed graph |
| savename: str |
| filename to be saved |
| |
| Returns |
| ------- |
| None |
| |
| """ |
| clean_degree = clean_adj.sum(1) |
| perturbed_degree = perturbed_adj.sum(1) |
| fig, ax1 = plt.subplots() |
| sns.distplot(clean_degree, label='Clean Graph', norm_hist=False, ax=ax1) |
| sns.distplot(perturbed_degree, label='Perturbed Graph', norm_hist=False, ax=ax1) |
| ax1.grid(False) |
| plt.legend(prop={'size':18}) |
| plt.ylabel('Density Distribution', fontsize=18) |
| plt.xlabel('Node degree', fontsize=18) |
| plt.xticks(fontsize=14) |
| plt.yticks(fontsize=14) |
| |
| if not os.path.exists('figures/'): |
| os.mkdir('figures') |
| plt.savefig('figures/%s' % savename, bbox_inches='tight') |
| plt.show() |
|
|
| def feature_diff(clean_adj, perturbed_adj, features, savename='feature_diff.pdf'): |
| """Plot feature difference on clean and perturbed graphs. |
| |
| Parameters |
| ---------- |
| clean_adj: sp.csr_matrix |
| adjancecy matrix of the clean graph |
| perturbed_adj: sp.csr_matrix |
| adjancecy matrix of the perturbed graph |
| features: sp.csr_matrix or np.array |
| node features |
| savename: str |
| filename to be saved |
| |
| Returns |
| ------- |
| None |
| """ |
|
|
| fig, ax1 = plt.subplots() |
| sns.distplot(_get_diff(clean_adj, features), label='Normal Edges', norm_hist=True, ax=ax1) |
| delta_adj = perturbed_adj - clean_adj |
| delta_adj[delta_adj < 0] = 0 |
| sns.distplot(_get_diff(delta_adj, features), label='Adversarial Edges', norm_hist=True, ax=ax1) |
| ax1.grid(False) |
| plt.legend(prop={'size':18}) |
| plt.ylabel('Density Distribution', fontsize=18) |
| plt.xlabel('Feature Difference Between Connected Nodes', fontsize=18) |
| plt.xticks(fontsize=14) |
| plt.yticks(fontsize=14) |
| if not os.path.exists('figures/'): |
| os.mkdir('figures') |
| plt.savefig('figures/%s' % savename, bbox_inches='tight') |
| plt.show() |
|
|
|
|
| def _get_diff(adj, features): |
| isSparse = sp.issparse(features) |
| edges = np.array(adj.nonzero()).T |
| row_degree = adj.sum(0).tolist()[0] |
| diff = [] |
| for edge in tqdm(edges): |
| n1 = edge[0] |
| n2 = edge[1] |
| if n1 > n2: |
| continue |
| d = np.sum((features[n1]/np.sqrt(row_degree[n1]) - features[n2]/np.sqrt(row_degree[n2])).power(2)) |
| diff.append(d) |
| return diff |
|
|
|
|
