Latest Articles for Graph Theory

Exploring the challenges of rearranging tokens in graphs.

New methods aim to improve graph layout evaluation accuracy.

A clear look into Slupecki digraphs and their unique properties.

Research highlights new methods for matchings in hypergraphs with conflict avoidance.

ARROW-Diff efficiently generates large, realistic graphs using random walks.

A look into incidence algebras and their representations in partially ordered sets.

A simpler approach to approximating the 2-ECSS problem in graph theory.

Efficient methods to reconstruct graphs using minimal queries in large networks.

Path-LLM offers new ways to create meaningful graph embeddings for diverse applications.

Research focuses on optimizing the stable set problem in various graph types.

A look into association schemes and their role in mathematical analysis.

A look at nested conditions and their impact on graph transformations.

EggNet enhances particle tracking accuracy using evolving graph-based techniques.

Exploring the unique properties of random spanning trees in graph theory.

A new method improves GNN architecture design using insights from language models.

This framework enhances predictions by focusing on real causal relationships in event data.

This article examines testing methods for ensuring accuracy in graph databases.

A look into the significance and statistical behavior of graph cuts.

Study clustering patterns in graph colouring with strong products and bounded properties.

Explore the significance and complexity of orthogonality dimension in graph theory.

A new algorithm improves the search for multiple shortest paths in directed graphs.

Exploring unique characteristics of regular graphs, 2-factors, and Hamiltonian circuits.

Exploring the connections between balanced incomplete block designs and generalized quadrangles.

A study on algorithm performance for the Max-Cut problem.

Two players strategize to control a graph's vertices in a dominator-staller game.

GrassNet uses State Space Models for adaptive filtering in graph neural networks.

Explore the basics and importance of graph structures and their properties.

Exploring the relationships between theta graphs and diffeomorphic mapping classes.

A study on descents, inversions, and their implications in permutations.

New methods boost graph node classification accuracy with limited labeled data.

This article investigates LLM performance on graph-related reasoning tasks.

A new method addresses challenges in Graph Neural Networks with heterophilic data.

Introducing GraphSPNs to enhance graph-based probabilistic tasks and molecule generation.

Explore the link between frames and graphs to understand scalability.

New polynomial methods simplify tree isomorphism verification.

This article examines how symmetrization links polytopes and posets in mathematics.

Learn how distance oracles optimize distance queries in large networks.

Learn how Graph Neural Networks improve quantum error correction techniques.

An overview of chromatic and cochromatic numbers in graph theory.

Exploring the role of quantum machine learning in advancing data analysis techniques.