# Topological Transforms

**TODO**: summarize from https://blog.derwen.ai/graph-levels-of-detail-ea4226abba55

Graph topological transform approaches so far (e.g., `lee2023ingram`

) have focused on using relation affinities to train *representation learning* models. this may be another example of using deep learning as a mêlée weapon. instead,

results computed from *graph of relations* analysis naturally feed into *statistical relational learning* approaches such as *probabilistic soft logic*, to develop rule sets and ground truth for training SRE models.

TODO: survey/compare topological decomposition of graphs, then using statistics to determine how to reconstruct probabilistically => for recomposition of generate graph elements (not simple nodes, edges)