kby24 / AIDD
Automated Discovery of Interactions and Dynamics for Large Networked Dynamical Systems
☆16Updated 3 years ago
Alternatives and similar repositories for AIDD
Users that are interested in AIDD are comparing it to the libraries listed below
Sorting:
- Neural Dynamics on Complex Networks☆53Updated 4 years ago
- Code for Neural Relational Inference with Efficient Message Passing Mechanisms (AAAI 2021).☆16Updated 4 years ago
- ☆46Updated 3 years ago
- Code and dataset to test empirically the expressive power of graph pooling operators presented as presented at NeurIPS 2023☆37Updated last year
- Gumbel Graph Network (GGN) : A General Deep Learning Framework for Network Reconstruction☆70Updated 5 years ago
- GraphCON (ICML 2022)☆59Updated 2 years ago
- This is the official repository for our paper KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning.☆42Updated last month
- ☆27Updated 3 years ago
- ☆31Updated last year
- Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."☆66Updated 3 years ago
- Code of "Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective" paper published in ICLR2021☆46Updated 3 years ago
- ☆26Updated 3 years ago
- Gradient gating (ICLR 2023)☆53Updated 2 years ago
- Bayesian Graph Neural Networks with Adaptive Connection Sampling - Pytorch☆58Updated 4 years ago
- An awesome collection of causality-inspired graph neural networks.☆76Updated 5 months ago
- PyTorch implementation of "Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited"☆39Updated last year
- ☆20Updated last year
- ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks(ICLR 2023)☆19Updated last year
- ☆13Updated 5 months ago
- NeurIPS 2021 paper 'Representation Learning on Spatial Networks' code☆18Updated 3 years ago
- Yuhong Luo and Pan Li. Neighborhood-aware scalable temporal network representation learning. In Learning on Graphs, 2022.☆27Updated 2 years ago
- Reference implementation for SPECTRE: Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators (ICML …☆26Updated 2 years ago
- Implementation for the paper: GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation☆27Updated 2 years ago
- [ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.☆167Updated last year
- ☆20Updated last year
- NeurIPS'22 Spotlight paper "Hierarchical Graph Transformer with Adaptive Node Sampling"☆50Updated last year
- ☆40Updated 2 years ago
- A library for subgraph GNN based on pyg☆41Updated 5 months ago
- ☆21Updated last year
- Code and data for the paper `Bayesian Semi-supervised Learning with Graph Gaussian Processes'☆38Updated 6 years ago