Nature

Graph Coloring Algorithms and Optimization Techniques

The graph colouring problem, a classic NP-hard challenge, is central to many practical applications such as scheduling, resource allocation and network management. Recent advances have seen the ...
Nature

B-Chromatic Number and Graph Coloring

The study of graph colouring has long been a central topic in discrete mathematics, with a prominent focus on optimising the assignment of labels or colours to vertices such that adjacent vertices are ...
BBC

Distance-time graphs

Each line segment of a distance-time graph represents one part of a journey. A distance-time graph, sometimes referred to as a travel graph, is a way of representing a journey. It is helpful to have ...
GitHub

Stack-Graphs Python bindings

Opinionated Python bindings for the tree-sitter-stack-graphs rust library. It exposes a minimal, opinionated API to leverage the stack-graphs library for reference resolution in source code. The rust ...
IEEE

Combining Reinforcement Learning and Heuristic Optimization: A Model Based on a Deep Q-Network and Graph Neural Networks for Graph Coloring

Abstract: We present a hybrid approach combining Reinforcement Learning (RL) with the TabuCol, which is a version of tabu search specifically designed for the Graph Coloring Problem (GCP), enhanced by ...
GitHub

Black Hole Strategy for Graph Sparsification in MOF Networks

The Black Hole (BH) strategy is a novel graph sparsification technique inspired by the gravitational pull of black holes, which condense matter into highly structured forms. BH retains the most ...
IEEE

A Multi-Graph Attention Spatial-Temporal Graph Convolutional Network (MGA-STGCN) for AHP Risk Forecasting

Abstract: We propose a Multi-graph Attention spatial-temporal graph convolutional network (MGA-STGCN) for AHP risk forecasting. To describe the temporal and spatial features of the area, we use ...