Adding edge thickness and node colors in NetworkX graph plotting

draw_networkx_graph.py

# saliency
sal = cluster_saliency[cluster_label] # [ (grad-norm, grad-max)
grad_max = sal[1] / max(sal[1])


feat_vertices = features[cluster_ids, :]
adj_mat = get_adjmat(feat_vertices, is_norm_adj=False)
adj_mat_normed = get_adjmat(feat_vertices, is_norm_adj=True)

# create networkx graph from adjacency matrix
g = nx.from_numpy_matrix(adj_mat_normed)

affinities  = {}
for edge in g.edges():
    affinities[edge] = adj_mat_normed[edge[0], edge[1]]
nx.set_edge_attributes(g, affinities, 'my_weight')

pos = nx.spring_layout(g, weight = 'my_weight')
edge_wts = [15.0 * adj_mat_normed[edge[0], edge[1]] for edge in g.edges()]

node_szs = [100*grad_max[node] for node in g.nodes()]  # proportional to "saliency"
# node_szs = [5.0 * adj_mat[node].sum() for node in g.nodes()]
n_colors = [grad_max[node] for node in g.nodes()]  # node color also proportional to saliency

plt.figure()

sc = nx.draw_networkx_nodes(G=g, pos = pos, node_list = g.nodes(), alpha=0.9, 
                        node_size = node_szs, node_color=n_colors, cmap='viridis')

nx.draw_networkx_edges(G = g, pos = pos, edge_color='k', alpha=0.6, width=edge_wts)

sc.set_norm(mcolors.LogNorm()) # log-scale colormap for better visualization and contrast
plt.colorbar(sc)

plt.savefig(osp.join(dist_cache, 'cluster_%d.png' % cluster_label))
plt.close()