Graph Neural Networks: a bibliometrics overview

TL;DR

This paper provides a comprehensive bibliometric overview of graph neural networks research since 2004, analyzing trends, influential authors, institutions, and hot topics in the field.

Contribution

It offers the first extensive bibliometric analysis of GNN research, highlighting publication trends, key contributors, and emerging research directions.

Findings

GNN research has grown significantly since 2004.

Key subject areas include computer science, engineering, and social sciences.

Hot topics include graph convolutional networks and attention mechanisms.

Abstract

Recently, graph neural networks have become a hot topic in machine learning community. This paper presents a Scopus based bibliometric overview of the GNNs research since 2004, when GNN papers were first published. The study aims to evaluate GNN research trend, both quantitatively and qualitatively. We provide the trend of research, distribution of subjects, active and influential authors and institutions, sources of publications, most cited documents, and hot topics. Our investigations reveal that the most frequent subject categories in this field are computer science, engineering, telecommunications, linguistics, operations research and management science, information science and library science, business and economics, automation and control systems, robotics, and social sciences. In addition, the most active source of GNN publications is Lecture Notes in Computer Science. The most prolific or impactful institutions are found in the United States, China, and Canada. We also provide must read papers and future directions. Finally, the application of graph convolutional networks and attention mechanism are now among hot topics of GNN research.