# Low-dimensional path representation learning

I have a graph (ex: map) and multiple sequences of ids representing different paths.

• A vertex represents a region/area
• An edge between 2 vertices : a crossing from a region to another
• A graph path (sequences of crossings) : a trajectory

Like the examples below:

path1 = [15,1,2,3]
path2 = [1,2,9]
path3 = [15,3]


All the paths come from the same graph structure and they could have various high sizes (~50). Then I would like to get a low-dimensional vector (one for each path) in order to perform an Approximate Neighbors Search (it's a kind of search technique to find out the closest data points to another).

I have found some papers about graph representation learning but nothing relevant. Should I explore an NLP technique or a graph embeddings technique?