Latest Articles for Graph Theory

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.

A new method enhances graph predictions with reliable uncertainty estimates.

CoNHD enhances hypergraph modeling for complex relationships with edge-dependent classification.

A method that customizes graph sparsification for better neural network performance.

Research presents algorithms for minimizing node distances in dynamically revealed graphs.

Exploring the link between soft happy colouring and community detection in networks.

A new method for improving graph data analysis and communication.

A new algorithm improves edge coloring speed and efficiency in graph theory.

A study of polytopes formed from multipartite graphs and their properties.

AGS-GNN improves GNNs by addressing challenges in heterophilic graphs.

A study on hypergraphs with high girth and their link to coding theory.

Investigating closed routes in graphs with specific visit constraints.

A new framework improves GNNs by using clustering to enhance learning.

A method to assess uncertainty in graph predictions using conformal prediction.

ARC provides a flexible solution for detecting anomalies across various graph datasets.

Learn how to compare phylogenetic networks using contractions and expansions.

Link2Doc enhances link prediction by merging text and graph structure.

A look at regularized projection matrix methods for improving clustering and community detection.

Introducing GRAG to enhance language models' accuracy using graph structures.

New methods improve how we analyze complex graph data structures.

Researchers investigate quantum algorithms for optimizing the Max-Cut problem efficiently.

A look at how distance matrices help rebuild trees and unicyclic graphs.

A deep dive into Littlewood identities and their significance in mathematics.