A Review on Graph Neural Network Methods in Financial Applications

11/27/2021
by   Jianian Wang, et al.
0

Keeping the individual features and the complicated relations, graph data are widely utilized and investigated. Being able to capture the structural information by updating and aggregating nodes' representations, graph neural network (GNN) models are gaining popularity. In the financial context, the graph is constructed based on real-world data, which leads to complex graph structure and thus requires sophisticated methodology. 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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/04/2021

Deep Graph Structure Learning for Robust Representations: A Survey

Graph Neural Networks (GNNs) are widely used for analyzing graph-structu...
research
05/01/2020

Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection

The graph-based model can help to detect suspicious fraud online. Owing ...
research
11/03/2020

GAIN: Graph Attention Interaction Network for Inductive Semi-Supervised Learning over Large-scale Graphs

Graph Neural Networks (GNNs) have led to state-of-the-art performance on...
research
02/12/2022

Improving Fraud detection via Hierarchical Attention-based Graph Neural Network

Graph neural networks (GNN) have emerged as a powerful tool for fraud de...
research
03/05/2023

Unlearnable Graph: Protecting Graphs from Unauthorized Exploitation

While the use of graph-structured data in various fields is becoming inc...
research
06/08/2021

Principled Hyperedge Prediction with Structural Spectral Features and Neural Networks

Hypergraph offers a framework to depict the multilateral relationships i...
research
12/14/2020

Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective

The re-ranking approach leverages high-confidence retrieved samples to r...

Please sign up or login with your details

Forgot password? Click here to reset