With the widespread digitization of finance and the increasing popularity of cryptocurrencies,
the sophistication of fraud schemes devised by cybercriminals is growing. Money laundering …

In recent years, Graph Neural Networks (GNNs) have made significant advances in
processing structured data. However, most of them primarily adopted a model-centric …

Fraud detection plays a crucial role in the financial industry, preventing significant financial
losses. Traditional rule-based systems and manual audits often struggle with the evolving …

We introduce a weighted and unconstrained variant of the well-known minimum union
problem: Given a bipartite graph with weights for all nodes in, find a set such that the ratio …

Link Prediction is a foundational task in Graph Representation Learning, supporting
applications like link recommendation, knowledge graph completion and graph generation …

Flight delays due to holding maneuvers are a critical and costly phenomenon in aviation,
driven by the need to manage air traffic congestion and ensure safety. Holding maneuvers …

The emerging discipline of Computational Science is concerned with using computers to
simulate or solve scientific problems. These problems span the natural, political, and social …

Graph Neural Networks (GNNs) have seen significant advances in recent years, yet their
application to multigraphs, where parallel edges exist between the same pair of nodes …

Combating money laundering has become increasingly complex with the rise of cybercrime
and digitalization of financial transactions. Graph-based machine learning techniques have …

The financial transaction relationships between existing entities are complex and diverse. In
this situation, traditional risk control methods mainly ignored such complex and implicit …