This paper presents MAPPING, a model-agnostic framework for debiasing Graph Neural Networks (GNNs) to ensure fair node classification and reduce leakage of sensitive information. Despite the success of GNNs in…
Continue readingThe paper presents Dual-Prism (DP), a new method for graph data augmentation that addresses issues like graph property distortions and restricted structural changes. DP, comprising DP-Noise and DP-Mask, preserves the…
Continue readingThe article ‘Towards Principled Graph Transformers’ discusses the expressive power of graph learning architectures, particularly the Edge Transformer, within the Weisfeiler Leman (k-WL) hierarchy. The study highlights the Edge Transformer’s…
Continue readingThe authors propose a Hybrid Time-Varying Graph Neural Network (HTVGNN) to improve traffic flow predictions. Traditional methods based on graph neural networks (GNNs) have limitations in capturing spatial correlations in…
Continue readingThe paper discusses the application of Graph Neural Networks (GNNs) in bolstering defensive cyber operations. With the growing complexity and evolution of cyber attacks, traditional defensive mechanisms are proving inadequate….
Continue readingThe article introduces a learnable Perception-Action-Communication (LPAC) architecture for decentralized navigation in robot swarms, focusing on the coverage control problem. The proposed solution involves a convolution neural network (CNN) that…
Continue readingThe article introduces a new framework called H2G2-Net, which uses multi-modal physiological signals to predict cognitive states. The authors argue that existing graph neural networks (GNNs) struggle with this type…
Continue readingThe article introduces Graph2Tac (G2T), a graph neural network that learns hierarchical representations of mathematical concepts in theorem proving. G2T uses a large-scale, graph-based dataset for machine learning in Coq,…
Continue readingThe study explores the expressive power of Graph Neural Networks (GNNs) and their application in fields like social science, chemistry, and medicine. The authors examine the theoretical properties of GNNs,…
Continue readingThis research paper introduces a new approach called DGDNN for predicting stock movement. Traditional methods often ignore the interdependencies between stocks and the hierarchical structure of the stocks themselves. The…
Continue readingLooks like you're using an ad blocker. Please support us by disabling these ad blocker.
