Latest Articles for Graph Theory

A look into matroids and their applications in various fields.

Examining the complexities of message delivery in connected graph networks.

A fast and efficient method for graph processing without complex models.

This article presents a novel way to study geodesic disks and their intersections.

This study explores how topology awareness affects Graph Neural Network performance and fairness.

Automated methods improve efficiency in extracting information from Knowledge Graphs.

This research examines relationships between extremal graphs, edge counts, and spectral properties.

A look into Wasserstein distance and its applications in probability theory.

A look into matroids, their properties, and intersection theory applications.

This study examines the interactions and properties of colored permutation groups.

A new method enhances GNNs by improving graph structure recognition.

A clear look at the key ideas in graph theory and their connections.

Analyzing four-valent oriented graphs and their symmetry properties.

Learn how GKEDM improves Graph Convolutional Network performance.

A study on intersection points of lines in del Pezzo surfaces of degree 1.

Understanding how Graph Neural Networks model complex relationships in data.

An approach to optimize edge 2-coloring for improved graph performance.

New strategies simplify image-to-graph transformation for various applications.

A look into posets, decompositions, and their significance in mathematics.

A look at how GNNs analyze graphs and adapt to changes.

A new framework enhances the efficiency of heterogeneous graph learning.

New circuit designs improve quantum walk implementations on current hardware.

GBC offers an efficient solution for counting bicliques in large bipartite graphs.

A new method improves GNN performance by utilizing explanation subgraphs.

A new method improves finding large defective cliques in complex graphs.

Learn to tackle complex graph problems using periodic sets and tree decomposition.

New framework enhances link prediction in knowledge graphs using language models.

Sublinear expanders offer unique connectivity properties for sparse graphs in various fields.

A new method improves anomaly detection in changing graphs by using spatial and temporal memory.

Examining non-homotopic drawings and their properties in multigraphs.

Combining quantum computing and machine learning to address complex graph challenges.

Research focuses on simplifying complex graph problems using expander techniques.

A look into minimal kinematics and its role in particle interactions.

A new method simplifies the study of random geometric graphs for deeper insights.

Study reveals factors affecting single-layer GCN's performance on various data models.

Recent studies reveal new insights into graph edge directions and their connectivity.

Examining walk matrices and cokernels reveals insights into random graph structures.

A look at how RGB-tilings help prove the Four Color Theorem.

An overview of contraction perfect graphs and their significance in graph theory.

Examining how graph rewriting systems operate through structured frameworks.