Ni, Pin; Yuan, QiAo; Khraishi, Raad; Okhrati, Ramin; Lipani, Aldo; Medda, Francesca; (2022) Eigenvector-based Graph Neural Network Embeddings and Trust Rating Prediction in Bitcoin Networks. In: Proceedings of the 3rd ACM International Conference on AI in Finance (ICAIF' 22). Association for Computing Machinery (ACM): New York, NY, USA. (In press).

Preview

Text Ni_icaif22-138.pdf Download (845kB) | Preview

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 which 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.

Download activity - last month

Export as JSONCSVXML

Download activity - last 12 months

Export as JSONCSVXML

Downloads by country - last 12 months

Export as CSVXMLJSON

Archive Staff Only

View Item