Alternatives and similar repositories for HoGRC:

Users that are interested in HoGRC are comparing it to the libraries listed below

• TrustAGI-Lab / MTGODE
  [TKDE 2022] The official PyTorch implementation of the paper "Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs".
  ☆80Updated 2 years ago
• Graph-Machine-Learning-Group / sgp
  Official repository for the paper "Scalable Spatiotemporal Graph Neural Networks" (AAAI 2023)
  ☆46Updated last year
• square-coder / STGODE
  Spatial-Temporal Graph ODE Neural Network
  ☆106Updated 3 years ago
• tsinghua-fib-lab / ResInf
  This repo contains the codes and data for our Nature Communications paper: Deep learning resilience inference for complex networked syste…
  ☆44Updated 4 months ago
• distributed-information-bottleneck / distributed-information-bottleneck.github.io
  A repository for using the distributed information bottleneck to locate information in data
  ☆15Updated 6 months ago
• calvin-zcx / ndcn
  Neural Dynamics on Complex Networks
  ☆51Updated 4 years ago
• yokifly / ASTTN_pytorch
  ☆36Updated 2 years ago
• m-serious / tipping-predictor
  Code to early predict the onset of critical transitions in networked dynamical systems.
  ☆17Updated 3 months ago
• Graph-Machine-Learning-Group / spin
  Official repository for the paper "Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations" (NeurIPS 202…
  ☆54Updated last year
• YimiAChack / STDDE
  Official implementation for "Unveiling Delay Effects in Traffic Forecasting: A Perspective from Spatial-Temporal Delay Differential Equat…
  ☆13Updated last month
• XDZhelheim / HimNet
  [KDD'24] Official code for our paper "Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting".
  ☆44Updated 6 months ago
• LiuAoyu1998 / STIDGCN
  Spatial–Temporal Dynamic Graph Convolutional Network With Interactive Learning for Traffic Forecasting
  ☆56Updated 4 months ago
• zbliu98 / GRAM-ODE
  Code for the TMLR 2023 paper "GRAM-ODE: Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting"
  ☆15Updated last year
• joeloskarsson / tgnn4i
  Code for our paper "Temporal Graph Neural Networks for Irregular Data"
  ☆18Updated last year
• Friger / GAGNN
  add codes
  ☆17Updated 3 years ago
• Z-GCNETs / Z-GCNETs
  ☆39Updated 3 years ago
• tongnie / ImputeFormer
  [KDD 2024] "ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation"
  ☆37Updated last month
• tamps2gcnets / TAMP_S2GCNets
  ☆19Updated 2 years ago
• WeizhuQIAN / DeepSTUQ_Pytorch
  ☆10Updated 9 months ago
• dtortorella / graph-esn
  Graph Echo State Networks for Pytorch
  ☆20Updated last year
• Graph-Machine-Learning-Group / taming-local-effects-stgnns
  Official repository for the paper "Taming Local Effects in Graph-based Spatiotemporal Forecasting" (NeurIPS 2023)
  ☆19Updated last year
• bnusss / GGN
  Gumbel Graph Network (GGN) : A General Deep Learning Framework for Network Reconstruction
  ☆70Updated 5 years ago
• idea-iitd / Frigate
  ☆15Updated last year
• Graph-Machine-Learning-Group / grin
  Official repository for the paper "Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks" (ICLR 2022)
  ☆162Updated 2 years ago
• wengwenchao123 / DDGCRN
  [Pattern Recognition] Decomposition Dynamic Graph Conolutional Recurrent Network for Traffic Forecasting
  ☆75Updated 2 months ago
• gorgen2020 / DVGNN
  ☆23Updated last year
• luokn / ms-gat
  Learning Multiaspect Traffic Couplings by Multirelational Graph Attention Networks for Traffic Prediction
  ☆14Updated 2 years ago
• tsinghua-fib-lab / Spatio-temporal-Diffusion-Point-Processes
  A diffusion-based framework for spatio-temporal point processes
  ☆66Updated 11 months ago
• tsinghua-fib-lab / DiskNet
  Official implementation for "Predicting Long-term Dynamics of Complex Networks via Identifying Skeleton in Hyperbolic Space" (KDD2024)
  ☆22Updated 5 months ago
• deepkashiwa20 / MegaCRN
  [AAAI23] This it the official github for AAAI23 paper "Spatio-Temporal Meta-Graph Learning for Traffic Forecasting"
  ☆112Updated last year