AlphaGalileo

A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications

The 2024 Nobel Prize in Chemistry was recently granted to David Baker, Demis Hassabis and John M. Jumper, renowned for their pioneering works in ...
IEEE

A Multi-Clustering Aggregation Method for Initializing Node Representations in Featureless Graphs

Abstract: The non-Euclidean nature of graphs made them inaccessible to standard deep learning techniques that rely on fixed-size, ordered inputs. Graph Neural Networks (GNNs) are essential for serving ...
GitHub

ML SDK Scenario Runner

The Scenario Runner is an application that executes shader and neural network graph workloads through Vulkan® or the ML extensions for Vulkan®. The Scenario Runner acts as a validation and performance ...
GitHub

QwQ2000/WSDM26-Graph-Unlearning-Inversion

This is the PyTorch implementation for our WSDM'26 paper "Unlearning Inversion Attack for Graph Neural Networks". Please find an early access version of our paper on ...
IEEE

Multigranularity Deep Graph Convolutional Neural Network Node Clustering Leveraging Spatial Information

Abstract: In the era of information explosion, clustering analysis of graph-structured data and empty graph-structured data is of great significance for extracting the intrinsic value of data. From ...
Frontiers

Multimodal graph neural networks in healthcare: a review of fusion strategies across biomedical domains

1 Department of Analytics, Harrisburg University of Science and Technology, Harrisburg, PA, United States 2 Department of Emergency Medicine, UMass Chan Medical School, Worcester, MA, United States ...