title: Clustering Graphs using graph distance
body: <p>What I'm currently doing is:</p>
  <ol>
  <li>Train a GNN and see which graphs are labelled wrongly compared to the ground truth.</li>
  <li>Use a GNN-explainer model to help explain which minimum sub-graph is responsible for the mislabeling by checking the wrongly label instances.</li>
  <li>Use the graph_edit_distance from networkx to see how much these graphs differentiate from another.</li>
  <li>See if I can find clusters that help explain why the GNN might label some graphs wrongly.</li>
  </ol>
  <p>Does this seem reasonable?
  How would I go around step 4? Would I use something like sklearn_extra.cluster.KMedoids?</p>
  <p>All help is appreciated!</p>