Latest Articles for Graph Theory

Explore how hidden orders in mathematics reveal deeper connections.

A look at algorithms for maintaining minimum cuts in changing graphs.

Research focuses on optimizing graph connections through terminals and Steiner nodes.

A look into the significance of posets and their polytopes in mathematics.

Research on multidimensional circulant graphs boosts quantum error correction methods.

GSANs improve data processing in complex structures like graphs and simplicial complexes.

A new framework improves accuracy in node classification with limited labeled data.

A new model that improves graph learning by focusing on node pairs.

A simple breakdown of weighted chordal graphs and their significance.

A new model tackles challenges in graph neural networks using power series.

This work presents methods for generating trees to model graphs effectively.

A framework for analyzing complex data using graph structures.

Unraveling the concept of resistance distance in connected networks.

This article studies maximal cliques across various random network models.

A look at vector spaces, special bases, and their role in group theory.

Exploring the blend of quantum and classical methods for solving the Max-Cut problem.

Discover new methods for analyzing signals on graphs effectively.

A new model enhances community search by integrating multi-dimensional edge attributes.

New techniques improve diameter calculations for unit-disk graphs, aiding efficient network analysis.

DeepRicci enhances graph neural networks by improving structure and node features.

A new method to color edges in specific sparse graphs with minimal colors efficiently.

Recent improvements in k-Clique algorithms enhance efficiency in identifying connected groups.

This article explores how cut-rank influences graph transformations and their applications.

This article examines the connections in friends-and-strangers graphs and their implications.

A new method for explaining Graph Neural Networks enhances transparency and trust.

A new method forecasts the structure of dynamic graphs over time.

GraphTempo helps study changes in graphs over time, enhancing analysis efficiency.

Learn how to analyze evolving graphs for key historical events.

Exploring the relationship between graphs and curves through quasistable divisors.

An overview of the Tutte polynomial and its application to greedoids.

A critical look at Eulerian graphs and related indices in research.

New framework tackles challenges in Graph Neural Networks effectively.

FedGT improves federated learning with enhanced privacy and performance for graph data.

Learn how feature networks enhance machine learning and data interpretation.

A look into pairable states and their role in quantum communication.

This study explores how causality enhances Graph Neural Networks' classification tasks.

Explore the significance of controllable graphs in mathematics and computer science.

An overview of graph types, properties, and their importance in various fields.

Discover how neighbor complexes reveal connections in random graphs.

A look into special edge-weighted graphs with unique properties.