AlphaGalileo

Adapting to the Stream: An Instance-Attention GNN Method for Irregular Multivariate Time Series Data

DynIMTS replaces static graphs with instance-attention that updates edge weights on the fly, delivering SOTA imputation and P12 classification ...
IEEE

MVF-GNN: Multi-View Fusion With GNN for 3D Semantic Segmentation

Abstract: Due to the high cost of obtaining 3D annotations and the accumulation of many 2D datasets with 2D semantic labels, deploying multi-view 2D images for 3D semantic segmentation has attracted ...
Frontiers

Graph neural networks in Alzheimer's disease diagnosis: a review of unimodal and multimodal advances

Alzheimer's Disease (AD), a leading neurodegenerative disorder, presents significant global health challenges. Advances in graph neural networks (GNNs) offer promising tools for analyzing multimodal ...
Scientific Research Publishing

An In-Depth Analysis of Graph Neural Networks and Machine Learning Approaches for Drug Repositioning ()

1 Department of Computer Science and Engineering, Kishoreganj University, Kishoreganj, Bangladesh. 2 Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh. 3 ...
EurekAlert!

Real-time, large-scale graph neural network inference through BingoCGN

BingoCGN employs cross-partition message quantization to summarize inter-partition message flow, which eliminates the need for irregular off-chip memory access and utilizes a fine-grained structured ...
Scientific Research Publishing

Gordon, D., Petousis, P., Zheng, H., Zamanzadeh, D. and Bui, A.A.T. (2021) TSI-GNN: Extending Graph Neural Networks to Handle Missing Data in Temporal Settings. Frontiers in ...

ABSTRACT: Missing data remains a persistent and pervasive challenge across a wide range of domains, significantly impacting data analysis pipelines, predictive modeling outcomes, and the reliability ...
GitHub

Explaining GNN Explanations With Edge Gradients

This repository contains code to reproduce the experiments in the KDD 2025 paper "Explaining GNN Explanations With Edge Gradients", including the Occlusion, Layerwise Occlusion, and Layerwise Gradient ...
IEEE

Anomaly Detection Over Multi-Relational Graphs Using Graph Structure Learning and Multi-Scale Meta-Path Graph Aggregation

Abstract: Graph Neural Networks (GNNs) have recently achieved remarkable success in various learning tasks involving graph-structured data. However, their application to multi-relational graph anomaly ...