Chunming Hu

GraphFed: A Personalized Subgraph Federated Learning Framework for Non-IID Graphs

2023

Recently, federated graph learning has attracted significant attention, as subgraphs of a global graph may often distribute across different institutions and are subject to privacy restrictions. However, inevitable data heterogeneity among sub-graphs can lead to …

GNNavigator: Towards Adaptive Training of Graph Neural Networks via Automatic Guideline Exploration

2024

Graph Neural Networks (GNNs) succeed significantly in many applications recently. However, balancing GNNs training runtime cost, memory consumption, and attainable accuracy for various applications is non-trivial. Previous training methodologies suffer from inferior adaptability and lack …

Prompt-based Unifying Inference Attack on Graph Neural Networks

2025 · Proceedings of the AAAI Conference on Artificial Intelligence

Graph neural networks (GNNs) provide important prospective insights in applications such as social behavior analysis and financial risk analysis based on their powerful learning capabilities on graph data. Nevertheless, GNNs' predictive performance relies on the …

Mitigating Message Imbalance in Fraud Detection with Dual-View Graph Representation Learning

2025 · arXiv (Cornell University)

Graph representation learning has become a mainstream method for fraud detection due to its strong expressive power, which focuses on enhancing node representations through improved neighborhood knowledge capture. However, the focus on local interactions leads …