An embedding-based distance for temporal graphs
Lorenzo Dall'Amico Alain Barrat Ciro Cattuto
We define a distance between temporal graphs based on graph embeddings built
using time-respecting random walks. We study both the case of matched graphs,
when there exists a known relation between the nodes, and the unmatched case,
when such a relation is unavailable and the graphs may be of different sizes.
We illustrate the interest of our distance definition, using both real and
synthetic temporal network data, by showing its ability to discriminate between
graphs with different structural and temporal properties. Leveraging
state-of-the-art machine learning techniques, we propose an efficient
implementation of distance computation that is viable for large-scale temporal
graphs.