Latest Articles for Graph Neural Networks

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

Exploring how transformers tackle various graph reasoning tasks effectively.

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

New framework improves predictions by analyzing interconnected data series.

A new approach enhances recommendation systems using text and images.

A new method improves communication in graph data processing.

A new framework combines GNNs and LLMs for improved answers from knowledge graphs.

Introducing GEM, a fast and accurate solution for Maxwell's equations using graph neural networks.

This approach improves function comparison in software through call graphlets.

GNAN combines interpretability and accuracy for graph-based AI models.

A new approach to boost GNNs against noise and attacks.

HOGE enhances explanations of Graph Neural Networks using cell complexes.

A novel approach to integrate transformers with graph structures for better outcomes.

Exploring strategies to improve Graph Neural Networks and tackle oversmoothing challenges.

This research focuses on enhancing GNNs using self-supervised learning and feature alignment.

New method improves predictions for lung cancer patients facing recurrence risk.

Graph neural networks speed up security assessments in power systems.

Introducing GENIE, a watermarking method for safeguarding Graph Neural Networks against theft.

This study introduces GNNs to improve online matching in various digital platforms.

A novel approach enhances detection of software security vulnerabilities using advanced models.

A new method for improving mobile signal mapping accuracy using advanced technology.

New method reveals vulnerabilities in GNN explanation methods.

New model enhances simulations of physical systems using machine learning techniques.

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

Analyzing renewable energy impacts on power grid stability using machine learning techniques.

Combining approaches to enhance predictions of substance interactions.

A new model offers better clarity and efficiency in filling knowledge graph gaps.

Introducing a fast and efficient system for retrieving CAD parts using graph neural networks.

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

P3GNN enhances APT detection while protecting data privacy in SDN networks.

Explaining GNN decisions using activation rules improves trust and understanding.

A new dual-transformer model enhances execution time predictions from source code analysis.

A novel method enhances data sharing for efficient GNN training.

New models enhance accuracy in predicting urban land use and improve planning.

CoDreamer enhances cooperation among agents in dynamic environments.

AMBER leverages machine learning to automate mesh generation for engineering simulations.

A new framework assesses graph condensation methods for improved machine learning efficiency.

A new technique enhances graph neural networks performance on large datasets.

Graph Neural Networks enhance histopathological image analysis, improving disease diagnosis.

Graphcodes enhance topological data analysis for better insights in complex datasets.