Code for the paper "How Attentive are Graph Attention Networks?" (ICLR'2022)
☆373Apr 5, 2022Updated 4 years ago
Alternatives and similar repositories for how_attentive_are_gats
Users that are interested in how_attentive_are_gats are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- [ICLR 2021] How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision☆159Apr 7, 2023Updated 3 years ago
- PyTorch implementation of MTAD-GAT (Multivariate Time-Series Anomaly Detection via Graph Attention Networks) by Zhao et. al (2020, https:…☆394Jan 16, 2024Updated 2 years ago
- Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)☆3,123Jul 6, 2023Updated 2 years ago
- Graph Transformer Architecture. Source code for "A Generalization of Transformer Networks to Graphs", DLG-AAAI'21.☆1,025Jul 27, 2021Updated 4 years ago
- The implementation for the NeurIPS 2022 paper Parameter-free Dynamic Graph Embedding for Link Prediction.☆16Dec 7, 2022Updated 3 years ago
- Managed Database hosting by DigitalOcean • AdPostgreSQL, MySQL, MongoDB, Kafka, Valkey, and OpenSearch available. Automatically scale up storage and focus on building your apps.
- How Powerful are Graph Neural Networks?☆1,285Jul 1, 2021Updated 5 years ago
- Recipe for a General, Powerful, Scalable Graph Transformer☆866Jul 4, 2024Updated 2 years ago
- Universal Graph Transformer Self-Attention Networks (TheWebConf WWW 2022) (Pytorch and Tensorflow)☆681Aug 16, 2022Updated 3 years ago
- Code of "Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective" paper published in ICLR2021☆46Jun 10, 2021Updated 5 years ago