2020-04-23 · Novel approaches based on the theme of structuring the representations and computations of neural network-based models in the form of a graph.

graph-neural-networks graph-convolutional-networks graph-auto-encoders relational-graph-convolutional-networks

2020-07-20 · A Temporal Extension Library for PyTorch Geometric

deep-learning graph-convolutional-networks graph-neural-networks representation-learning

Efficient and Friendly Graph Neural Network Library for TensorFlow 1.x and 2.x.

graph-neural-networks graph-convolutional-networks tensorflow code

2019-12-04 · A Comprehensive Survey on Graph Neural Networks.

graph-neural-networks graph-convolutional-networks graph-autoencoders spatial-temporal-gnns

2020-04-07 · Source code for the paper: ProteinGCN: Protein model quality assessment using Graph Convolutional Networks.

protein-model-quality-estimation graph-convolutional-networks graph-neural-networks pytorch

An article explaining Graph Convolutional Networks as simply as possible.

graph-convolutional-networks graph-neural-networks geometric-deep-learning graphs

2020-06-24 · MPQE is a model for answering complex queries over knowledge graphs, that learns embeddings of entities in the knowledge graph, & embeddings for variable ...

graph-neural-networks graph-convolutional-networks knowledge-graphs mpqe

Geometric deep learning extension library for PyTorch.

graph-neural-networks graph-convolutional-networks pytorch geometric

2019-11-11 · ASAP is a sparse and differentiable pooling method that addresses the limitations of previous graph pooling layers.

graph-neural-networks graph-classification pool self-attention

2020-05-07 · We propose a simple framework (GraphEDM) and a comprehensive Taxonomy to review and unify several graph representation learning methods.

graph-neural-networks graph-convolutional-networks autoencoders graph-regularization

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