IEEE

GIFTS: Efficient GCN Inference Framework on PyTorch-CPU via Exploring the Sparsity

Abstract: Graph Convolutional Networks (GCNs) are gaining attraction in AI research due to their ability to learn from graph data effectively. However, deploying GCNs on CPUs presents substantial ...
IEEE

Multiscale Adjacency Matrix CNN: Learning on Multispectral LiDAR Point Cloud via Multiscale Local Graph Convolution

Abstract: Multispectral LiDAR can rapidly acquire 3D and spectral information of objects, providing richer features for point cloud semantic segmentation. Despite the remarkable performance of ...
GitHub

This repository is an official PyTorch (Geometric) implementation of FDGNN in "Disentangled contrastive learning for fair graph representations".

@article{zhang2025disentangled, title={Disentangled contrastive learning for fair graph representations}, author={Zhang, Guixian and Yuan, Guan and Cheng, Debo and Liu, Lin and Li, Jiuyong and Zhang, ...
istockphoto

Graph paper. Printable squared grid paper with color horizontal lines. Geometric background for school, textures, notebook, diary. Realistic lined paper blank size reversal A5 ...

Royalty-free licenses let you pay once to use copyrighted images and video clips in personal and commercial projects on an ongoing basis without requiring additional payments each time you use that ...
GitHub

Open-set Cross-network Node Classification via Unknown-excluded Adversarial Graph Domain Alignment (UAGA)

This repository contains the author's implementation in PyTorch for the paper "Open-set Cross-network Node Classification via Unknown-excluded Adversarial Graph Domain Alignment". data/ contains the 3 ...