Latest Articles for Graph Theory

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.

A new approach enhances graph embeddings by focusing on dimension management.

This article discusses how GRSNN enhances graph reasoning tasks using synaptic delay.

Examining how in-distribution labels impact out-of-distribution detection in machine learning.

Study the interplay between Iwasawa theory and branched towers in graph theory.

A framework improves GNNs by using text attributes in graph structures.

Exploring how transformers tackle various graph reasoning tasks effectively.

Exploring new methods for designing frames in machine learning.

Research tackles the black hole search problem in changing networks.

Learn how spanners can maintain distance accuracy despite edge failures.

Introducing MAGE: an innovative approach to explain Graph Neural Networks in chemistry.

This article explores the relationship between magnitude homology and path homology in directed graphs.

A new approach enhances querying in graph databases using ontologies.

Discover how random hyperbolic graphs represent real-world networks effectively.

Analyzing the significance of graph Laplacians in connectivity and structure.

This study presents methods to recover magnitude-symmetric matrices based on principal minors.

A look into triply efficient shadow tomography for quantum systems.

A fresh approach to keeping sensitive graph data safe online.

A new model for predicting links in dynamic graphs with clear explanations.