Graph Neural Networks for Node-Level Predictions

TL;DR

This paper reviews the development of graph neural networks for node-level prediction, highlighting key methods, benchmarks, applications, and open challenges in analyzing complex graph-structured data.

Contribution

It provides a comprehensive overview of GNN-based methods for node prediction, including core concepts, influential convolutional techniques, and practical applications.

Findings

Overview of core GNN concepts and methods

Introduction of common benchmarks for evaluation

Discussion of open problems and future research directions

Abstract

The success of deep learning has revolutionized many fields of research including areas of computer vision, text and speech processing. Enormous research efforts have led to numerous methods that are capable of efficiently analyzing data, especially in the Euclidean space. However, many problems are posed in non-Euclidean domains modeled as general graphs with complex connection patterns. Increased problem complexity and computational power constraints have limited early approaches to static and small-sized graphs. In recent years, a rising interest in machine learning on graph-structured data has been accompanied by improved methods that overcome the limitations of their predecessors. These methods paved the way for dealing with large-scale and time-dynamic graphs. This work aims to provide an overview of early and modern graph neural network based machine learning methods for node-level prediction tasks. Under the umbrella of taxonomies already established in the literature, we explain the core concepts and provide detailed explanations for convolutional methods that have had strong impact. In addition, we introduce common benchmarks and present selected applications from various areas. Finally, we discuss open problems for further research.