How can I examine the subnetworks of a nearly fully connected graph? I have an almost fully connected graph in python with roughly 3k nodes and 9M edges. Each node in this graph is represented by a point in R3 and each edge represents the distance between them with a weight given by a weight matrix.
My goal is to visualize subnetworks (areas where points close to each other also have a dense set of edge weights) where the subnetwork is colored by the given weight. However, this is extremely computationally expensive, as there are 9M edges. I'm looking for an algorithm that can either return the subnetworks, visualize these subnetworks, or something similar.
It sounds like you don't need to be talking about this as a &quot;graph&quot; data structure.. you have points in R3 and you want to partition them into clusters based on their distances (or their pairwise weight matrix). This is more a statistical clustering problem than a graph clustering problem.
Here are some python functions to accomplish these:
https://scikit-learn.org/stable/modules/clustering.html