PyTorch Implementation for "Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space (KDD2021)"

A curated collection of research papers exploring the utilization of LLMs for graph-related tasks.

[NeurIPS 2024] Spiking Graph Neural Networks on Riemannian Manifolds

Some GNNs are implemented using PyG for node classification tasks, including: GCN, GraphSAGE, SGC, GAT, R-GCN and HAN (Heterogeneous Graph Attention Network), which will continue to be updated in t…

Some GNNs are implemented using PyG for link prediction tasks, including: GCN, GraphSAGE, GAT, Node2Vec、RGCN、HGT and HAN, which will continue to be updated in the future.

The official implementation of Non-separable Spatio-temporal Graph Kernels via SPDEs.

2024年保研经验贴和相关物料

2024年计算机保研夏令营&冬令营通知

Motif-aware Riemannian Graph Neural Network with Generative-Contrastive Learning

Institution: North China Electric Power University-Sunrise Lab. Deep learning Repository for study.

PyTorch implementation of Pseudo-Riemannian Graph Convolutional Networks (NeurIPS'22))

Code for paper Fully Hyperbolic Neural Networks

Source code for WWW 2021 paper "Lorentzian Graph Convolutional Networks"

Hyperbolic Graph Convolutional Networks in PyTorch.

Hyperbolic Graph Neural Networks

基于深度强化学习的原神自动钓鱼AI