23 citations · 47 across the 5 of their papers we have counts for
5 papers
Global Confidence Degree Based Graph Neural Network for Financial Fraud Detection
Jiaxun Liu, Yue Tian, Guanjun Liu
Graph Neural Networks (GNNs) are widely used in financial fraud detection due to their excellent ability on handling graph-structured financial data and modeling multilayer connect…
Transaction Fraud Detection via an Adaptive Graph Neural Network
Yue Tian, Guanjun Liu, Jiacun Wang +1
Many machine learning methods have been proposed to achieve accurate transaction fraud detection, which is essential to the financial security of individuals and banks. However, mo…
Enhancing the Robustness of QMIX against State-adversarial Attacks
Weiran Guo, Guanjun Liu, Ziyuan Zhou +2
Deep reinforcement learning (DRL) performance is generally impacted by state-adversarial attacks, a perturbation applied to an agent's observation. Most recent research has concent…
Partially Observable Mean Field Multi-Agent Reinforcement Learning Based on Graph-Attention
Min Yang, Guanjun Liu, Ziyuan Zhou
Traditional multi-agent reinforcement learning algorithms are difficultly applied in a large-scale multi-agent environment. The introduction of mean field theory has enhanced the s…
Petri Nets-based Methods on Automatically Detecting for Concurrency Bugs in Rust Programs
Kaiwen Zhang, Guanjun Liu
Rust's memory safety guarantees, notably ownership and lifetime systems, have driven its widespread adoption. Concurrency bugs still occur in Rust programs, and existing detection…