Latest Articles for Graph Theory

New method GSSC enhances graph analysis efficiency and effectiveness.

Cometh combines discrete and continuous methods for efficient graph generation.

Examining temporal graphs reveals complexities and methods for efficient problem-solving.

Exploring the links between torus knots and generalized Schroder paths.

A new framework explains node embeddings using simple graph features.

This study focuses on efficient methods for t-matchings in restricted graphs.

An overview of Ramsey numbers and their significance in directed graphs.

Explore the concept of dicoloring and its significance in directed graphs.

An overview of maximum matching concepts, challenges, and algorithms in graphs.

New GNN architecture improves predictions through enhanced expressiveness and fragment integration.

This research tackles oversmoothing in graph convolutions to improve node classification accuracy.

This study reveals connections between graphs, their subgraphs, and independence numbers.

A new method improves processing of text-rich graphs for better insights.

This study evaluates spectral GNNs for effectiveness and efficiency across various tasks.

Explore the significance of graph polynomials in mathematics and computer science.

A look into saturated hypergraphs and their significance in random networks.

A new method enhances efficiency and flexibility in processing Subgraph GNNs.

OLGA effectively classifies graph nodes for one-class learning tasks using innovative techniques.

A new method enhances graph classification using motifs and subgraphs.

This study highlights the need for improved inductive KGC datasets.

Learn how to update strongly connected components in dynamic planar graphs.

Explore the structure and applications of IOD Graphs in various fields.

Exploring digraphs, their homology, and key algebraic structures.

A new approach to enhancing neural networks through rule-based layers for better data integration.

IGL-Bench provides essential tools for better analyzing imbalanced graphs.

A new benchmark addresses biases in link prediction evaluations for better real-world applications.

HiFGL framework addresses challenges in privacy-focused collaborative learning.

A new measure, GGD, improves how we assess similarities and differences in graphs.

This study reveals the properties and applications of normal matrices and balanced graphs.

Exploring the significance of spectral radii in graphs and their applications.

A guide to control methods in diffusively-coupled networks for better outcomes.

A new benchmark for evaluating dynamic text-attributed graphs in various fields.

This article explores -DAG-width and its applications in directed graphs.

A new approach for better edge classification using topological aspects.

Explaining GNN decisions using activation rules improves trust and understanding.

This article examines how pre-trained models learn about relationships via hypergraphs.

Examining the p-Laplacian's role in data analysis and complex systems.

Explore the significance and applications of graph coloring in various fields.

A look at the Bethe approximation's role in predicting outcomes in complex systems.

A look into the structure and significance of short cycles in planar graphs.