Generate lots of random graphs with scalar features on each node.

random_scalar_graph.py

import networkx as nx
import numpy as np

def generate_graph():
    num_nodes = np.random.randint(low=3, high=20)
    G = nx.erdos_renyi_graph(n=num_nodes, p=0.3)
    for n in G.nodes():
        value = np.random.randint(low=1, high=20)
        G.node[n]['value'] = value
    return G

G = generate_graph()

def features(G):
    values = [d['value'] for n, d in G.nodes(data=True)]
    return np.array(values).reshape(-1, 1)

n_graphs = 30

graphs = []
amats = []
feats = []
sums = []
for i in range(n_graphs):
    g = generate_graph()
    graphs.append(g)
    a = nx.adjacency_matrix(g).todense()
    amats.append(a)
    f = features(g)
    feats.append(f)
    s = np.ones(shape=(1, a.shape[0]))
    sums.append(s)
    
import scipy.sparse as sp

a = sp.block_diag(amats)
f = np.vstack(feats)
s = sp.block_diag(sums)
sum([d['value'] for n, d in graphs[0].nodes(data=True)])

sums[0] @ feats[0]
s @ f