Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview

TL;DR

This overview discusses various graph neural network models tailored for small graphs and large networks, highlighting their architectures and applications in representation learning for graph data.

Contribution

It provides a comprehensive survey of recent GNN models designed for different graph sizes, clarifying their suitability and architectural differences.

Findings

Different GNN models are suited for small vs. large graphs.

The paper categorizes GNN architectures based on graph size.

It summarizes recent advances in GNNs for various graph types.

Abstract

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.