Abstract: Many modern applications are modeled using graphs of some kind. Given a graph, assigning labels (usually called colors) to vertices is called graph coloring. Colors must be assigned so that ...

The fastest Python implementation of the ForceAtlas2 graph layout algorithm, with Cython optimization for 10-100x speedup. Supports NetworkX, igraph, and raw adjacency matrices. ForceAtlas2 is a force ...

This repository contains code for our SPAA paper "Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable" (SPAA'18). It includes implementations of the following parallel graph ...

This article introduces a model-based design, implementation, deployment, and execution methodology, with tools supporting the systematic composition of algorithms from generic and domain-specific ...

As a teenager in the Czech Republic, Lenka Zdeborová glimpsed her future in an Isaac Asimov novel. A character in Asimov’s “Foundation” series invents a mathematical method for predicting the path of ...

Abstract: The Graph Coloring Problem (GCP) is concerned with finding the chromatic number, i.e., the minimum number of unique colors required to color adjacent nodes in the graph. Given that ...

Brief: Researchers from the Computing and Computational Sciences Directorate (CCSD) at Oak Ridge National Laboratory (ORNL) have developed a distributed implementation of graph convolutional neural ...

In this paper, we present two divide-and-conquer algorithms for clustering large graphs. Both algorithms apply a base algorithm on several small subgraphs and then use these individual local ...

Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important ...

Deciding whether two graphs are structurally identical, or isomorphic, is a classical algorithmic problem that has been studied since the early days of computing. Applications span a broad field of ...