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