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We introduce tf_geometric, an efficient and friendly library for graph deeplearning, which is compatible with both TensorFlow 1.x and 2.x. tf_geometricprovides kernel libraries for building Graph Neural Networks (GNNs) as well asimplementations of popular GNNs. The kernel libraries consist ofinfrastructures for building efficient GNNs, including graph data structures,graph map-reduce framework, graph mini-batch strategy, etc. Theseinfrastructures enable tf_geometric to support single-graph computation,multi-graph computation, graph mini-batch, distributed training, etc.;therefore, tf_geometric can be used for a variety of graph deep learning tasks,such as transductive node classification, inductive node classification, linkprediction, and graph classification. Based on the kernel libraries,tf_geometric implements a variety of popular GNN models for different tasks. Tofacilitate the implementation of GNNs, tf_geometric also provides some otherlibraries for dataset management, graph sampling, etc. Different from existingpopular GNN libraries, tf_geometric provides not only Object-OrientedProgramming (OOP) APIs, but also Functional APIs, which enable tf_geometric tohandle advanced graph deep learning tasks such as graph meta-learning. The APIsof tf_geometric are friendly, and they are suitable for both beginners andexperts. In this paper, we first present an overview of tf_geometric'sframework. Then, we conduct experiments on some benchmark datasets and reportthe performance of several popular GNN models implemented by tf_geometric.

Efficient Graph Deep Learning in TensorFlow with tf_geometric