Latest Articles for Graph Theory

Explore key issues affecting GNN performance in link prediction tasks.

Exploring methods to list minimal dominating sets in graphs and hypergraphs.

A look at minimum spanning trees formed by randomly placed points and their weight behavior.

A new framework for generating clear explanation graphs from natural language.

This article explores the relationship between event structures and full graphs using data mining.

This study categorizes Nakayama algebras through visual quipu structures and their relations.

A systematic approach to check conjugacy in right-angled Artin groups.

A novel framework enhances vulnerability detection using advanced graph techniques.

A new method to enhance graph datasets for improved model performance.

Researchers develop a model to create graphs directly from natural language descriptions.

Learn how algorithms tackle the k-MST problem efficiently.

Exploring the connections between graph theory and geometric spaces to solve complex problems.

A look into zero-divisor graphs and their mathematical significance.

GC-Flow improves clustering in graph data through integrated classification and clustering techniques.

Examines how different node types affect Graph Neural Networks' performance.

Path queries play a vital role in extracting meaningful data from graph databases.

A new method enhances how we compare geometric graphs efficiently.

Learn about efficient methods for calculating Personalized PageRank Vectors in large graphs.

Exploring growth sequences in periodic graphs using Ehrhart theory.

A look at GNNs and their role in solving complex optimization problems.

A new GNN architecture improves attention mechanisms for better performance in deep layers.

A new algorithm efficiently updates shortest paths in changing networks.

An overview of metric spaces and their key properties.

A look at how oscillator-based machines tackle complex optimization problems.

Examining KKT points in the Motzkin-Straus program reveals insights into graph structure.

Cacti play a crucial role in network analysis and geometric research.

New method improves machine learning performance across different data environments.

Techniques to control synchronization in networks, focusing on targeted node selection.

A study of protection numbers in trees and their significance in mathematics.

New methods enhance entity alignment in knowledge graphs using similarity flooding techniques.

A look into the contextual stochastic block model and its implications for graph analysis.

Coherent distributions merge expert opinions for better predictions on uncertain events.

This study presents a new method for augmenting graphs using randomized Schur complements.

Learn how stack and queue numbers shape graph arrangements.

Exploring the complexity of crossing numbers in almost planar and anchored planar graphs.

GShot generates high-quality graphs from limited training data using meta-learning techniques.

Exploring machine learning methods for network optimization challenges.

This article discusses directed 1-separations and their role in infinite digraphs.

A new method improves how GNNs explain their predictions.

This study focuses on the eigenvalue properties of connected 4-regular graphs.