Generalized Degrees for Scalable Discrete Time Dynamic Graph Generation

Kjartan van Driel, Leonardo Niccolò Ialongo, Pablo Andrés Astudillo, Stefan Thurner

Keywords: Scalable Graph Generation, Graph Generation, Dynamic Graphs, Temporal Graphs

Abstract:

The evolution of many real-world systems is best described by dynamic graphs,
whose statistical properties reflect the constraints of the system. When forecasting
their dynamics, the goal is to generate a time series of graphs respecting these
underlying constraints. Existing scalable dynamic graph learning methods,
however, are designed for local tasks such as link prediction or node classification,
and their independent, local predictions are ill-suited for graph generation. This
limitation is particularly relevant for discrete time dynamic graphs, where coarse
time resolution induces dependencies among edges within each snapshot. We
propose using a generalized notion of degrees to model such dependencies
directly, thereby shifting the focus from individual links to node dynamics. This
approach bypasses the need to learn a sparse graph representation, and yields an
inductive representation that enables the generation of large-scale discrete-time
dynamic graphs.

Submission Type: Extended abstract (max 4 main pages).

Submission Number: 94