  Graph neural networks denote a group of neural network models introduced for
the representation learning tasks on graph data specifically. Graph neural
networks have been demonstrated to be effective for capturing network structure
information, and the learned representations can achieve the state-of-the-art
performance on node and graph classification tasks. Besides the different
application scenarios, the architectures of graph neural network models also
depend on the studied graph types a lot. Graph data studied in research can be
generally categorized into two main types, i.e., small graphs vs. giant
networks, which differ from each other a lot in the size, instance number and
label annotation. Several different types of graph neural network models have
been introduced for learning the representations from such different types of
graphs already. In this paper, for these two different types of graph data, we
will introduce the graph neural networks introduced in recent years. To be more
specific, the graph neural networks introduced in this paper include IsoNN,
SDBN, LF&ER, GCN, GAT, DifNN, GNL, GraphSage and seGEN. Among these graph
neural network models, IsoNN, SDBN and LF&ER are initially proposed for small
graphs and the remaining ones are initially proposed for giant networks
instead. The readers are also suggested to refer to these papers for detailed
information when reading this tutorial paper.
