Graph statistics

karateclub.py

import networkx as nx
import numpy as np
import torch
from sklearn.preprocessing import StandardScaler

# load graph from networkx library
G = nx.karate_club_graph()

# retrieve the labels for each node
labels = np.asarray([G.nodes[i]['club'] != 'Mr. Hi' for i in G.nodes]).astype(np.int64)

# create edge index from 
adj = nx.to_scipy_sparse_matrix(G).tocoo()
row = torch.from_numpy(adj.row.astype(np.int64)).to(torch.long)
col = torch.from_numpy(adj.col.astype(np.int64)).to(torch.long)
edge_index = torch.stack([row, col], dim=0)

# using degree as embedding
embeddings = np.array(list(dict(G.degree()).values()))

# normalizing degree values
scale = StandardScaler()
embeddings = scale.fit_transform(embeddings.reshape(-1,1))