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cs.LG2026
From Few-Shot to Zero-Shot: Towards Generalist Graph Anomaly Detection
Yixin Liu, Shiyuan Li, Yu Zheng +4
Graph anomaly detection (GAD) is critical for identifying abnormal nodes in graph-structured data from diverse domains, including cybersecurity and social networks. The existing GA…
cs.LG2025
Adaptive Tokenization: On the Hop-Overpriority Problem in Tokenized Graph Learning Models
Zhibiao Wang, Yunlong Zhou, Ziwei Zhang +4
Graph Transformers, leveraging the global attention to capture long-range dependencies in graph structures, have significantly advanced graph machine learning, but face prohibitive…
cs.LG2024
FedPFT: Federated Proxy Fine-Tuning of Foundation Models
Zhaopeng Peng, Xiaoliang Fan, Yufan Chen +5
Adapting Foundation Models (FMs) for downstream tasks through Federated Learning (FL) emerges a promising strategy for protecting data privacy and valuable FMs. Existing methods fi…