Open-set graph learning is a practical task that aims to classify the known class nodes and
to identify unknown class samples as unknowns. Conventional node classification methods …

As the popularity of cryptocurrencies grows, the threat of phishing scams on trading
networks is growing. Detecting unusual transactions within the complex structure of these …

Cryptocurrencies have experienced a surge in popularity, paralleled by an increase in
phishing scams exploiting their transactional networks. Therefore, detecting anomalous …

Fraud detection for imbalanced datasets is challenging due to machine learning models
inclination to learn the majority class. Imbalance in fraud detection datasets affects how …

Ethereum is being utilized in various ways, including smart contracts and payments.
Research in cryptocurrency payments has either been general, about all cryptocurrencies or …

Blockchain technology revolutionizes the Internet, but also poses increasing risks,
particularly in cryptocurrency finance. On the Ethereum platform, Ponzi schemes, phishing …

Like any other useful technology, cryptocurrencies are sometimes used for criminal
activities. While transactions are recorded on the blockchain, there exists a need for a more …

Abstract Graph Neural Networks (GNNs) have demonstrated exceptional performance
across a wide range of graph-related applications. However, GNN models are susceptible to …

The advent of smart contracts has enabled the rapid rise of Decentralized Finance (DeFi) on
the Ethereum blockchain, offering substantial rewards in financial innovation and inclusivity …

The financial industry has become essential in the modern era, as nearly all individuals
have to interact with a bank, neither physically nor digitally. Online fraud happens when …