Test-time adaptation (TTA) has been a widely-studied paradigm for adapting well-trained models to distributionally shifted test data. Recent works extend TTA to graphs using graph augmentations and self-supervised objectives in Euclidean space. However, graph-structured data often exhibit heterogeneity where semantic s...
Ling-Wei Wei, Dou Hu, Li Sun et al.· Proceedings of the 32nd ACM...· 0 citations
Large language models (LLMs) have achieved remarkable success across diverse applications, yet their generic training paradigm limits effectiveness in user-specific scenarios. LLM personalization aims to adapt large models to individual users or user groups by incorporating preferences, histories, and contextual signal...
Rui-Jie Wang, Qing-Kai Zeng, Xuefei Wang et al.· Proceedings of the 32nd ACM...· 0 citations
Built upon local differential privacy (LDP), locally private graph learning protocols have emerged as an important paradigm for decentralized graph learning, balancing privacy protection and learning utility. Under such protocols, each user locally perturbs their node features and adjacency information before transmiss...
It is shown that this felt penalty becomes behaviorally binding through SPARC, a byte-clean code-gated reflection mechanism: LLM merchants fabricate when lying is free but restrain themselves when fabrication costs them sales, a self-interested response rather than compliance.
This survey presents the first comprehensive and systematic review of Differentially Private Graph Learning (DPGL), and organizes existing DPGL methods into four categories based on the granularity of privacy protection, namely node-level, edge-level, graph-level, and node-level.
Li Sun, Long-Zhu He, Ming Li et al.· Transactions on Graph Intell...· 1 citation
PPGNN, a personalized differentially private framework for decentralized graph data, enables user-specific privacy budgets during local perturbation while preserving analytical utility in decentralized graph learning scenarios.
Longzhu He, Peng Tang, Chaozhuo Li et al.· IEEE Transactions on Knowled...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
