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Eigenvector-based Graph Neural Network Embeddings and Trust Rating Prediction in Bitcoin Networks

Authors:

Francesca MeddaAuthors Info & Claims

ICAIF '22: Proceedings of the Third ACM International Conference on AI in Finance

Pages 27 - 35

https://doi.org/10.1145/3533271.3561793

Published: 26 October 2022 Publication History

Abstract

Given their strong performance on a variety of graph learning tasks, Graph Neural Networks (GNNs) are increasingly used to model financial networks. Traditional GNNs, however, are not able to capture higher-order topological information, and their performance is known to degrade with the presence of negative edges that may arise in many common financial applications. Considering the rich semantic inference of negative edges, excluding them as an obvious solution is not elegant. Alternatively, another basic approach is to apply positive normalization, however, this also may lead to information loss. Our work proposes a simple yet effective solution to overcome these two challenges by employing the eigenvectors with top-k largest eigenvalues of the raw adjacency matrix for pre-embeddings. These pre-embeddings contain high-order topological knowledge together with the information on negative edges, which are then fed into a GNN with a positively normalized adjacency matrix to compensate for its shortcomings. Through comprehensive experiments and analysis, we empirically demonstrate the superiority of our proposed solution in a Bitcoin user reputation score prediction task.

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Cited By

Saggu KBranco PJourdan G(2024)Improving GNN-Based Methods for Scam Detection in Bitcoin Transactions - A Practical Case Study2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA61799.2024.10722808(1-10)Online publication date: 6-Oct-2024
https://doi.org/10.1109/DSAA61799.2024.10722808
Cheng DYe YXiang SMa ZZhang YJiang C(2023)Anti-Money Laundering by Group-Aware Deep Graph LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327239635:12(12444-12457)Online publication date: 2-May-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3272396

Index Terms

Eigenvector-based Graph Neural Network Embeddings and Trust Rating Prediction in Bitcoin Networks
1. Applied computing
  1. Electronic commerce
    1. Secure online transactions

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Published In

cover image ACM Other conferences

ICAIF '22: Proceedings of the Third ACM International Conference on AI in Finance

November 2022

527 pages

ISBN:9781450393768

DOI:10.1145/3533271

Editors:
Daniele Magazzeni
J.P. Morgan AI Research
,
Senthil Kumar
Capital One
,
Rahul Savani
University of Liverpool
,
Renyuan Xu
University of Southern California
,
Carmine Ventre
King's College London
,
Blanka Horvath
University of Oxford
,
Ruimeng Hu
University of California Santa Barbara
,
Tucker Balch
J.P. Morgan AI Research
,
Francesca Toni
Imperial College London

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Published: 26 October 2022

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ICAIF '22: 3rd ACM International Conference on AI in Finance

November 2 - 4, 2022

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Cited By

Saggu KBranco PJourdan G(2024)Improving GNN-Based Methods for Scam Detection in Bitcoin Transactions - A Practical Case Study2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA61799.2024.10722808(1-10)Online publication date: 6-Oct-2024
https://doi.org/10.1109/DSAA61799.2024.10722808
Cheng DYe YXiang SMa ZZhang YJiang C(2023)Anti-Money Laundering by Group-Aware Deep Graph LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.327239635:12(12444-12457)Online publication date: 2-May-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3272396

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