  We introduce tf_geometric, an efficient and friendly library for graph deep
learning, which is compatible with both TensorFlow 1.x and 2.x. tf_geometric
provides kernel libraries for building Graph Neural Networks (GNNs) as well as
implementations of popular GNNs. The kernel libraries consist of
infrastructures for building efficient GNNs, including graph data structures,
graph map-reduce framework, graph mini-batch strategy, etc. These
infrastructures 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, link
prediction, and graph classification. Based on the kernel libraries,
tf_geometric implements a variety of popular GNN models for different tasks. To
facilitate the implementation of GNNs, tf_geometric also provides some other
libraries for dataset management, graph sampling, etc. Different from existing
popular GNN libraries, tf_geometric provides not only Object-Oriented
Programming (OOP) APIs, but also Functional APIs, which enable tf_geometric to
handle advanced graph deep learning tasks such as graph meta-learning. The APIs
of tf_geometric are friendly, and they are suitable for both beginners and
experts. In this paper, we first present an overview of tf_geometric's
framework. Then, we conduct experiments on some benchmark datasets and report
the performance of several popular GNN models implemented by tf_geometric.
