My thought is that if you have a set of graphs and graphs can be represented as adjacency matrices or lists, then essentially you can represent these graphs as a collection of dictionaries or data frames. Then you concatenate these into on giant dictionary or data frame.

For example, say you have 2 graphs each with nodes {a,b,c}. Then you could have a data frame with three columns and three rows for each that consist of the adjacency matrix. Add a new column to each to determine with graph is which (say column named “graph” and for the first graph it would 1 and 2 for second and so one). Then you can concatenate these matrices in a row wise manner to get a 6 row 4 column data frame where the columns are a,b,c, and graph. Then you can use whatever clustering technique you want.

I haven’t tried this but this would be my initial approach. Hope this helps and gives a new way of looking at the data!

I am also curious to see if others think this would be a valid approach as well!