Ao Liu

HeteroSample: Meta-Path Guided Sampling for Heterogeneous Graph Representation Learning

2024 · IEEE Internet of Things Journal

The rapid expansion of Internet of Things (IoT) has resulted in vast, heterogeneous graphs that capture complex interactions among devices, sensors, and systems. Efficient analysis of these graphs is critical for deriving insights in IoT …

Grimm: A Plug-and-Play Perturbation Rectifier for Graph Neural Networks Defending against Poisoning Attacks

2024 · arXiv (Cornell University)

Recent studies have revealed the vulnerability of graph neural networks (GNNs) to adversarial poisoning attacks on node classification tasks. Current defensive methods require substituting the original GNNs with defense models, regardless of the original's type. …

Graph Agent Network: Empowering Nodes with Inference Capabilities for Adversarial Resilience

2025 · Proceedings of the AAAI Conference on Artificial Intelligence

End-to-end training with global optimization have popularized graph neural networks (GNNs) for node classification, yet inadvertently introduced vulnerabilities to adversarial edge-perturbing attacks. Adversaries can exploit the inherent opened interfaces of GNNs' input and output, perturbing …

A Survey on Multimodal Large Language Models for Autonomous Driving

2024

With the emergence of Large Language Models (LLMs) and Vision Foundation Models (VFMs), multimodal AI systems benefiting from large models have the potential to equally perceive the real world, make decisions, and control tools as …