rollingstonezz / SGMP_code
NeurIPS 2021 paper 'Representation Learning on Spatial Networks' code
☆17Updated 3 years ago
Related projects ⓘ
Alternatives and complementary repositories for SGMP_code
- ☆44Updated 3 years ago
- Graph Information Bottleneck (GIB) for learning minimal sufficient structural and feature information using GNNs☆128Updated last year
- PyTorch code for "Learning Temporal Attention in Dynamic Graphs with Bilinear Interactions"☆98Updated 3 years ago
- ☆62Updated 4 years ago
- Source code for PairNorm (ICLR 2020)☆76Updated 4 years ago
- Neural Dynamics on Complex Networks☆51Updated 4 years ago
- Boost learning for GNNs from the graph structure under challenging heterophily settings. (NeurIPS'20)☆97Updated 2 years ago
- Source code for "Factorizable Graph Convolutional Networks", NeurIPS'20☆51Updated 3 years ago
- Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."☆65Updated 2 years ago
- Graph Representation Learning via Graphical Mutual Information Maximization☆111Updated 4 years ago
- ☆51Updated 5 years ago
- PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"☆86Updated 3 years ago
- PyTorch implementation of "Graph Convolutional Networks for Graphs Containing Missing Features"☆47Updated 9 months ago
- The official implementation for ICLR22 paper "Handling Distribution Shifts on Graphs: An Invariance Perspective"☆82Updated last year
- The implementation of our NeurIPS 2020 paper "Graph Geometry Interaction Learning" (GIL)☆45Updated 4 years ago
- ☆34Updated 2 years ago
- ☆54Updated 3 years ago
- Variational Graph Recurrent Neural Networks - PyTorch☆114Updated 4 years ago
- ☆73Updated 3 years ago
- The code for our ICLR paper: StructPool: Structured Graph Pooling via Conditional Random Fields☆57Updated 4 years ago
- Bayesian Graph Neural Networks with Adaptive Connection Sampling - Pytorch☆56Updated 4 years ago
- Graph meta learning via local subgraphs (NeurIPS 2020)☆119Updated 3 months ago
- How Powerful are Spectral Graph Neural Networks☆70Updated last year
- [ICML 2020] "When Does Self-Supervision Help Graph Convolutional Networks?" by Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen☆110Updated 3 years ago
- ☆33Updated 3 years ago
- Variational Graph Convolutional Networks☆22Updated 4 years ago
- Code of "Breaking the Limits of Message Passing Graph Neural Networks" paper published in ICML2021☆40Updated 3 years ago
- [ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.☆160Updated 9 months ago
- An implementation of KDD paper "Graph Convolutional Networks with EigenPooling"☆47Updated 4 years ago