Beyond Erdos-Renyi: Generalization in Algorithmic Reasoning on Graphs

Dobrik Georgiev Georgiev, Pietro Lio, Jakub Bachurski, Junhua Chen, Tunan Shi, Lorenzo Giusti

Keywords: graph neural networks, algorithmic reasoning

TL;DR: Algorithmic reasoning is often trained and tested on ER graphs only, so we decided to check if both are sensible thing to do.

Abstract:

Neural algorithmic reasoning excels in many graph algorithms, but assessment
mainly focuses on the Erdős-Rényi (ER) graph family. This study delves into
graph algorithmic models' generalization across diverse distributions. Testing
a leading model exposes overreliance on ER graphs for generalization
assessment. We further investigate two scenarios: generalisation to every
target distribution and single target distributions. Our results suggest that
achieving the former is not as trivial and achieving the latter can be aided
selecting source distribution via novel Tree Mover's Distance interpretation.

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

Submission Number: 60