IEEE

FedHeGA: A Federated Learning Framework for Enhanced Node Classification on Heterogeneous Graphs

Abstract: Heterogeneous graph neural networks (HGNNs) have proven effective at capturing complex relationships in graphs with diverse node and edge types. However, centralized training in HGNNs raises ...
GitHub

Graph Neural Networks for Node Classification

A production-ready implementation of Graph Neural Networks for node classification tasks, featuring multiple architectures (GCN, GAT, GraphSAGE, GIN) with comprehensive evaluation and interactive ...
Frontiers

Multi-Scale graph wavelet convolutional network for hyperspectral image classification

Hyperspectral images (HSIs) have very high dimensionality and typically lack sufficient labeled samples, which significantly challenges their processing and analysis. These challenges contribute to ...
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 ...