xpuoxford / L2G-neurips2021Links
☆26Updated 3 years ago
Alternatives and similar repositories for L2G-neurips2021
Users that are interested in L2G-neurips2021 are comparing it to the libraries listed below
Sorting:
- Codebase for Attentive Neural Hawkes Process (A-NHP) and Attentive Neural Datalog Through Time (A-NDTT)☆60Updated 7 months ago
- ☆22Updated 5 years ago
- ☆47Updated 3 years ago
- Implementation of "Fast and Flexible Temporal Point Processes with Triangular Maps" (Oral @ NeurIPS 2020)☆23Updated last year
- Official code for the ICML 2021 paper "Generative Causal Explanations for Graph Neural Networks."☆67Updated 3 years ago
- Implementation of "Intensity-Free Learning of Temporal Point Processes" (Spotlight @ ICLR 2020)☆85Updated 4 years ago
- Paper lists for Temporal Point Process☆113Updated last month
- Reinforced Causal Explainer for Graph Neural Networks, TPAMI2022☆36Updated 3 years ago
- ☆50Updated 2 years ago
- Energetic GraphNeural Networks (EGNN) implementation based on Dirichlet Energy Constrained Learning.☆27Updated 3 years ago
- Variational Graph Convolutional Networks☆23Updated 4 years ago
- ☆18Updated 4 years ago
- Code of "Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective" paper published in ICLR2021☆46Updated 4 years ago
- Neural Dynamics on Complex Networks☆53Updated 4 years ago
- A PyTorch Implementation of Neural Hawkes Process. Redefined.☆34Updated 5 years ago
- This is the official code repository for "Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs", wh…☆88Updated last year
- Source code for PairNorm (ICLR 2020)☆79Updated 5 years ago
- PyTorch code of "SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks"☆88Updated 3 years ago
- ☆60Updated 4 years ago
- ☆51Updated 3 years ago
- Discrete Graph Structure Learning for Forecasting Multiple Time Series, ICLR 2021.☆175Updated 3 years ago
- ☆101Updated last year
- Code for Transformer Hawkes Process, ICML 2020.☆196Updated last year
- Source code for NeurIPS 2019 paper "Learning Latent Processes from High-Dimensional Event Sequences via Efficient Sampling""☆10Updated 4 years ago
- ☆40Updated 3 years ago
- [ICLR'22] [KDD'22] [IJCAI'24] Implementation of "Graph Condensation for Graph Neural Networks"☆140Updated 9 months ago
- The official implementation of DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks (NeurIPS 2021)☆26Updated 3 years ago
- [TPAMI 2022] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*,…☆125Updated 3 years ago
- ☆46Updated last year
- Graph meta learning via local subgraphs (NeurIPS 2020)☆126Updated last year