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 ...
IEEE

EDG-Net: Edge-Enhanced Dynamic Graph Convolutional Network for Remote Sensing Scene Classification of Mining-Disturbed Land

Abstract: Scene classification and mapping of surface mining-disturbed land can attain semantic-level information that is useful for monitoring mine geo-environment. Mining land’s complex ...
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 ...
GitHub

talslsels/drugdiscovery-gnn-bbb-classification

This project presents three commonly used graph-based neural network architectures for molecular modeling: GCNConv, GINEConv, and TransformerConv. The primary focus is on model architecture design, ...
marktechpost

LangGraph Tutorial: A Step-by-Step Guide to Creating a Text Analysis Pipeline

LangGraph is a powerful framework by LangChain designed for creating stateful, multi-actor applications with LLMs. It provides the structure and tools needed to build sophisticated AI agents through a ...