Latest Articles for Graph Theory

This article explores -DAG-width and its applications in directed graphs.

A new approach for better edge classification using topological aspects.

Explaining GNN decisions using activation rules improves trust and understanding.

This article examines how pre-trained models learn about relationships via hypergraphs.

Examining the p-Laplacian's role in data analysis and complex systems.

Explore the significance and applications of graph coloring in various fields.

A look at the Bethe approximation's role in predicting outcomes in complex systems.

A look into the structure and significance of short cycles in planar graphs.

A new perspective on enhancing GNNs for complex graph structures.

This article explores the dynamics of rowmotion and whirling in partially ordered sets.

Examining the resilience of k-local quantum systems amidst external disturbances.

A look into techniques for estimating cuts in directed graphs.

A look at the role of tree walks in understanding random graphs.

A new model enhances node classification by leveraging related knowledge.

Discover the fascinating world of posets and their real-world applications.

A novel method uses random walks for efficient graph clustering.

A new method improves memory access for dynamic graph applications, boosting performance and efficiency.

A look into the structure and properties of hypergraph algebras.

A deep dive into toric ideals and their relation to graph structures.

A study on Steiner cuts and their applications in dynamic graph changes.

New methods improve model learning using effective negative samples in graph data.

ERIC offers an efficient way to measure graph similarities with improved accuracy.

This work enhances privacy in graph diffusion while maintaining data utility.

A study examines how well LLMs reason with graph data.

A look into maximal cliques and minimal transversals in graphs.

Explore the nature and significance of automatic sequences in mathematics.

This study investigates stable configurations in outer-totalistic cellular automata on random regular graphs.

A new dataset assesses LLM performance in solving graph-related challenges.

Learn about drags and their rewriting process in graphs.

This study investigates the rigidity of eigenvalues in random regular graphs amidst structural changes.

Exploring new methods in graph learning without labeled data.

A new method to efficiently map changing graphs while maintaining distance accuracy.

A novel framework enhances graph matching without labeled data.

A new framework assesses graph condensation methods for improved machine learning efficiency.

A groundbreaking model handles dynamic graphs while boosting performance and reducing training time.

Explore how chromatic numbers behave in dense random graphs and their implications.

A new technique enhances graph neural networks performance on large datasets.

Examining sparse outerstring graphs reveals efficient solutions for complex problems.

Explore how degree sequences affect random graph characteristics.

A look into Ramsey numbers, hypergraphs, and coloring in combinatorics.