Sen Su

Local Geometry Improves Explanation Robustness for Graph Neural Networks

Are LLM-Enhanced GNNs Privacy-Safe?

A systematic evaluation of privacy risks in LLM-enhanced GNNs through a unified framework consisting of five stages and reveals that semantic enrichment amplifies link-, label-, and membership-related signals in the embedding space, making them more exploitable by inference attacks.

Differentially Private Graph Learning: A Survey

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.

Toward Personalized Differentially Private Learning for Decentralized Local Graphs

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.