Applying the Neural Bellman-Ford Model to the Single Source Shortest Path Problem

Spyridon Drakakis, Constantine Kotropoulos

2024

Abstract

The Single Source Shortest Path problem aims to compute the shortest paths from a source node to all other nodes on a graph. It is solved using deterministic algorithms such as the Bellman-Ford, Dijkstra’s, and A* algorithms. This paper addresses the shortest path problem using a Message-Passing Neural Network model, the Neural Bellman Ford network, which is modified to conduct Predecessor Prediction. It provides a roadmap for developing models to calculate true optimal paths based on user preferences. Experimental results on real-world maps produced by the Open Street Map package show the ability of a Graph Neural Network to imitate the Bellman-Ford algorithm and solve the Single-Source Shortest Path problem.

Paper Citation

in Harvard Style

Drakakis S. and Kotropoulos C. (2024). Applying the Neural Bellman-Ford Model to the Single Source Shortest Path Problem. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 386-393. DOI: 10.5220/0012425800003654

in Bibtex Style

@conference{icpram24,
author={Spyridon Drakakis and Constantine Kotropoulos},
title={Applying the Neural Bellman-Ford Model to the Single Source Shortest Path Problem},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={386-393},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012425800003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Applying the Neural Bellman-Ford Model to the Single Source Shortest Path Problem
SN - 978-989-758-684-2
AU - Drakakis S.
AU - Kotropoulos C.
PY - 2024
SP - 386
EP - 393
DO - 10.5220/0012425800003654
PB - SciTePress