Graph representation learning in bioinformatics: trends, methods and applications
Graph is a natural data structure for describing complex systems, which contains a set of
objects and relationships. Ubiquitous real-life biomedical problems can be modeled as …
objects and relationships. Ubiquitous real-life biomedical problems can be modeled as …
Distributed graph neural network training: A survey
Graph neural networks (GNNs) are a type of deep learning models that are trained on
graphs and have been successfully applied in various domains. Despite the effectiveness of …
graphs and have been successfully applied in various domains. Despite the effectiveness of …
Towards better dynamic graph learning: New architecture and unified library
We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning.
DyGFormer is conceptually simple and only needs to learn from nodes' historical first-hop …
DyGFormer is conceptually simple and only needs to learn from nodes' historical first-hop …
Pytorch geometric temporal: Spatiotemporal signal processing with neural machine learning models
We present PyTorch Geometric Temporal, a deep learning framework combining state-of-the-
art machine learning algorithms for neural spatiotemporal signal processing. The main goal …
art machine learning algorithms for neural spatiotemporal signal processing. The main goal …
Graph representation learning and its applications: A survey
Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …
representation learning is a significant task since it could facilitate various downstream …