Neural Acceleration for Graph Partitioning

TL;DR

This paper introduces a neural network-based method to approximate the Fiedler vector, significantly speeding up spectral graph partitioning while maintaining high quality results.

Contribution

It presents a novel neural acceleration technique for spectral bisection, reducing computational costs in graph partitioning tasks.

Findings

Achieves comparable partition quality to traditional spectral bisection.

Reduces computational overhead significantly for large graphs.

Enhances scalability and efficiency of graph partitioning methods.

Abstract

Graph Partitioning is a critical problem in numerous scientific and engineering domains including social network analysis, VLSI design, and many more. Spectral methods are known to produce quality partitions while minimizing edge cuts for a wide range of problems. However, the computational cost associated with the calculation of the Fiedler vector, an eigenvector associated with the second smallest eigenvalue of the graph Laplacian, remains a significant bottleneck due to memory issues and computational costs. In this paper, we present an accelerated approach to spectral bisection partitioning by replacing the traditional eigenvalue calculation with a simple artificial neural network model to approximate the Fiedler vector. We demonstrate that our approach achieves partitioning quality comparable to spectral bisection while significantly reducing the computational overhead, making it more scalable and efficient for large-scale problems