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Journal of Data Science


A Review on Graph Neural Network Methods in Financial Applications
Volume 20, Issue 2 (2022), pp. 111–134
Pub. online: 2 May 2022      Type: Data Science Reviews      Open accessOpen Access
Received
3 November 2021
Accepted
10 April 2022
Published
2 May 2022

Abstract

With multiple components and relations, financial data are often presented as graph data, since it could represent both the individual features and the complicated relations. Due to the complexity and volatility of the financial market, the graph constructed on the financial data is often heterogeneous or time-varying, which imposes challenges on modeling technology. Among the graph modeling technologies, graph neural network (GNN) models are able to handle the complex graph structure and achieve great performance and thus could be used to solve financial tasks. In this work, we provide a comprehensive review of GNN models in recent financial context. We first categorize the commonly-used financial graphs and summarize the feature processing step for each node. Then we summarize the GNN methodology for each graph type, application in each area, and propose some potential research areas.

Supplementary material

 Supplementary Material
In the supplementary materials, we present materials that are not covered in the main text. The supplementary materials contain the summary table for each financial application, figures categorizing major GNN methodologies for each graph type and acronyms used in the text.

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2022 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
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Keywords
deep learning finance graph convolutional network graph representation learning

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