A holy grail of theoretical computer science, with numerous fundamental implications to more applied areas of computing such as operations research and artificial intelligence, is the question of ...

Graph partitioning and bisection problems occupy a central position in combinatorial optimisation and theoretical computer science. These issues involve dividing a graph’s vertex set into distinct ...

Distributed algorithms for graph problems represent a vibrant area of study that addresses the challenges of decentralised computation across interconnected networks. By partitioning complex graph ...

Graph sparsification is the approximation of an arbitrary graph by a sparse graph. We explain what it means for one graph to be a spectral approximation of another and review the development of ...

Abstract: In recent years, there has been a significant increase in the application of graph neural networks on a wide range of different problems. A specially promising direction of research is on ...

This repository provides partial datasets and the automated code for generating graph problem instances as described in the paper "GTA: Graph Theory Agent and Benchmark for Algorithmic Graph Reasoning ...

Graph processing at hyperscale has historically been a challenge because of the sheer complexity of algorithms and graph workflows. Alibaba has been tackling this issue via a project called GraphScope ...

Abstract: In many application domains, data are represented using large graphs involving millions of vertices and edges. Graph analysis algorithms, such as finding short paths and isomorphic subgraphs ...

This project represents some independent study I did when learning about Constraint Satisfaction Problems in AI. This code was written to help understand and demonstrate the following optimization ...