Latest Articles for Graph Theory

Inductive GNNs face privacy threats from link stealing attacks.

Examining how to uniquely pack three copies of 2-factors in graph theory.

New techniques simplify the calculation of the Wishart normalising constant in Bayesian models.

Exploring self-avoiding walks and their implications on complex graph structures.

A look into Ramsey and Turan theories in graph structures.

Efficient algorithms and heuristics for evaluating scanwidth in directed acyclic graphs.

This study examines the Weisfeiler-Leman dimension and coherent configurations in graphs.

This study examines when monochromatic subgraph counts show normal distribution patterns.

Innovative methods in counting subgraphs using graph kernels for various applications.

This article examines how edge density affects triangle formation in random graphs.

A novel approach to enhance graph consistency through transformation rules and application conditions.

A new method enhances the reliability of GCNs against adversarial changes.

Examining the relationships and complexity of Weihrauch degrees through equational theory.

Explore particle movement and its mathematical pathways in a structured space.

This paper presents new algorithms for managing interval selection in continuous data streams.

Explore the significance of tree-packing in computer science and its applications.

Developing algorithms for effective triangle sampling in graph data streams.

A deep dive into rainbow saturation numbers and edge-coloring challenges in graph theory.

Learn how random projections aid in analyzing complex relationships in large data sets.

A new model improves graph generation with discrete-state and continuous-time methods.

This article reveals minimal cellular resolutions for path ideals in graph theory.

Examining total coloring strategies for fullerene nanodiscs and their properties.

A strategy to protect sensitive connections in graph data while releasing useful information.

A framework to assess graph containers and their performance in algorithms.

New methods improve efficiency in identifying communities within evolving graphs.

New polynomial filters improve graph analysis performance across various applications.

New methods for graph clustering allow for flexible grouping without knowing cluster numbers.

Free sets help in arranging planar graphs without edge crossings.

A new method enhances clarity and performance of GNN predictions.

GNN-Diff improves GNN training efficiency and performance by optimizing hyperparameters.

A new approach to finding independent sets in graphs using any starting configuration.

A look into the challenges and solutions of the 2-means clustering problem.

This article presents -monoids, expanding the concept of free monoids into higher dimensions.

A novel approach improves efficiency in knowledge graph completion tasks.

An overview of classical and quantum approaches to network reliability.

Virtual nodes help improve performance in graph neural networks by enhancing information flow.

Examining K-Cores and degeneracy in graph streaming reveals significant complexity.

An overview of hypermaps and their significance in mathematics and science.

New method improves data removal in dynamic graph models while ensuring privacy.

Exploring the relationship between non-intersecting paths and Pfaffians in mathematics.