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Xiangyi Teng

3 accepted papers

2026

SFGA: Similarity-Constrained Fusion Learning for Unsupervised Anomaly Detection in Multiplex Graphs

AAAI 2026technical

Multiplex graphs are widely used to model multi-relational complex systems and play an important role in various real-world scenarios, such as financial systems and social networks. Hence, detecting anomalous samples in multiplex graph becomes crucial to ensure cybersecurity and stability. Although

Cited by 0SourcePDFScholar
2025

AutoSGNN: Automatic Propagation Mechanism Discovery for Spectral Graph Neural Networks

AAAI 2025technical

In real-world applications, spectral Graph Neural Networks (GNNs) are powerful tools for processing diverse types of graphs. However, a single GNN often struggles to handle different graph types—such as homogeneous and heterogeneous graphs—simultaneously. This challenge has led to the manual design…

2022

Robust Path Planner for Autonomous Vehicles on Roads With Large Curvature

RA-L 2022

Path planning in real road traffic refers to the navigation of an autonomous vehicle through an obstacle-filled environment. It is crucial for the comfort, safety, and efficiency of the autonomous driving experience. It is advantageous to plan paths in the reference line-based Frenet frames rather t

Cited by 25SourceScholar