Implementing graph neural networks with TensorFlow-Keras [article]

Patrick Reiser, Andre Eberhard, Pascal Friederich
2021 arXiv   pre-print
Graph neural networks are a versatile machine learning architecture that received a lot of attention recently. In this technical report, we present an implementation of convolution and pooling layers for TensorFlow-Keras models, which allows a seamless and flexible integration into standard Keras layers to set up graph models in a functional way. This implies the usage of mini-batches as the first tensor dimension, which can be realized via the new RaggedTensor class of TensorFlow best suited
more » ... r graphs. We developed the Keras Graph Convolutional Neural Network Python package kgcnn based on TensorFlow-Keras that provides a set of Keras layers for graph networks which focus on a transparent tensor structure passed between layers and an ease-of-use mindset.
arXiv:2103.04318v1 fatcat:qin3mmux2ffn7jctegd2ykxvku