From what I understand about clusters, they can be obtained from an existing graph at 1 instance of time. But consider the situation of a temporal network, such as a social network, where the graph keeps evolving and the clusters keep on changing.
So I'm looking for a method which will be able to give me a similarity measure between two set of clusters formed at different timestamps.
I have looked at a few research papers but haven't come across a proper implementation/algo related to the idea.
The idea mentioned here: https://math.stackexchange.com/questions/52184/measure-similarity-of-graphs is that of degree sequence and euclidean distance but works only on two graphs with matching number of nodes.
In a temporal-evolving network, a graph with constant nodes isn't really possible.
So, I'm looking for algorithm/suggestions to improve the existing methods which is easy to compute (and has some sort of implementation available, hopefully) that can find a similarity measure between two sets of clusters.