Temporal Graphs

\textsc{“Perseus”}: Tracing the Masterminds Behind Cryptocurrency Pump-and-Dump Schemes ArXiv ID: 2503.01686 “View on arXiv” Authors: Unknown Abstract Masterminds are entities organizing, coordinating, and orchestrating cryptocurrency pump-and-dump schemes, a form of trade-based manipulation undermining market integrity and causing financial losses for unwitting investors. Previous research detects pump-and-dump activities in the market, predicts the target cryptocurrency, and examines investors and \ac{“osn”} entities. However, these solutions do not address the root cause of the problem. There is a critical gap in identifying and tracing the masterminds involved in these schemes. In this research, we develop a detection system \textsc{“Perseus”}, which collects real-time data from the \acs{“osn”} and cryptocurrency markets. \textsc{“Perseus”} then constructs temporal attributed graphs that preserve the direction of information diffusion and the structure of the community while leveraging \ac{“gnn”} to identify the masterminds behind pump-and-dump activities. Our design of \textsc{“Perseus”} leads to higher F1 scores and precision than the \ac{“sota”} fraud detection method, achieving fast training and inferring speeds. Deployed in the real world from February 16 to October 9 2024, \textsc{“Perseus”} successfully detects $438$ masterminds who are efficient in the pump-and-dump information diffusion networks. \textsc{“Perseus”} provides regulators with an explanation of the risks of masterminds and oversight capabilities to mitigate the pump-and-dump schemes of cryptocurrency. ...

CaT-GNN: Enhancing Credit Card Fraud Detection via Causal Temporal Graph Neural Networks ArXiv ID: 2402.14708 “View on arXiv” Authors: Unknown Abstract Credit card fraud poses a significant threat to the economy. While Graph Neural Network (GNN)-based fraud detection methods perform well, they often overlook the causal effect of a node’s local structure on predictions. This paper introduces a novel method for credit card fraud detection, the \textbf{"\underline{Ca"}}usal \textbf{"\underline{T"}}emporal \textbf{"\underline{G"}}raph \textbf{"\underline{N"}}eural \textbf{“N”}etwork (CaT-GNN), which leverages causal invariant learning to reveal inherent correlations within transaction data. By decomposing the problem into discovery and intervention phases, CaT-GNN identifies causal nodes within the transaction graph and applies a causal mixup strategy to enhance the model’s robustness and interpretability. CaT-GNN consists of two key components: Causal-Inspector and Causal-Intervener. The Causal-Inspector utilizes attention weights in the temporal attention mechanism to identify causal and environment nodes without introducing additional parameters. Subsequently, the Causal-Intervener performs a causal mixup enhancement on environment nodes based on the set of nodes. Evaluated on three datasets, including a private financial dataset and two public datasets, CaT-GNN demonstrates superior performance over existing state-of-the-art methods. Our findings highlight the potential of integrating causal reasoning with graph neural networks to improve fraud detection capabilities in financial transactions. ...

Topology-Agnostic Detection of Temporal Money Laundering Flows in Billion-Scale Transactions ArXiv ID: 2309.13662 “View on arXiv” Authors: Unknown Abstract Money launderers exploit the weaknesses in detection systems by purposefully placing their ill-gotten money into multiple accounts, at different banks. That money is then layered and moved around among mule accounts to obscure the origin and the flow of transactions. Consequently, the money is integrated into the financial system without raising suspicion. Path finding algorithms that aim at tracking suspicious flows of money usually struggle with scale and complexity. Existing community detection techniques also fail to properly capture the time-dependent relationships. This is particularly evident when performing analytics over massive transaction graphs. We propose a framework (called FaSTMAN), adapted for domain-specific constraints, to efficiently construct a temporal graph of sequential transactions. The framework includes a weighting method, using 2nd order graph representation, to quantify the significance of the edges. This method enables us to distribute complex queries on smaller and densely connected networks of flows. Finally, based on those queries, we can effectively identify networks of suspicious flows. We extensively evaluate the scalability and the effectiveness of our framework against two state-of-the-art solutions for detecting suspicious flows of transactions. For a dataset of over 1 Billion transactions from multiple large European banks, the results show a clear superiority of our framework both in efficiency and usefulness. ...