On the pursuit of graph embedding strategies for individual mobility networks

An Individual Mobility Network (IMN) is a graph representation of the mobility history of an individual that highlights the relevant locations visited (nodes of the graph) and the movements across them (edges), also providing a rich set of annotations of both nodes and edges. Extracting representative features from an IMN has proven to be a valuable task for enabling various learning applications. However, it is also a demanding operation that does not guarantee the inclusion of all important aspects from the human perspective. A vast recent literature on graph embedding goes in a similar direction, yet typically aims at general-purpose methods that might not suit specific contexts. In this paper, we discuss the existing approaches to graph embedding and the specificities of IMNs, trying to find the best matching solutions. We experiment with representative algorithms and study the results in relation to IMN characteristics. Tests are performed on a large dataset of real vehicle trajectories.