Data-centric AI, with its primary focus on the collection, management, and utilization of data
to drive AI models and applications, has attracted increasing attention in recent years. In this …

Despite recent progress in Graph Neural Networks (GNNs), explaining predictions made by
GNNs remains a challenging open problem. The leading method mainly addresses the local …

Many real-world datasets can be naturally represented as graphs, spanning a wide range of
domains. However, the increasing complexity and size of graph datasets present significant …

Graph Neural Networks (GNNs) have shown to be powerful tools for graph analytics. The
key idea is to recursively propagate and aggregate information along the edges of the given …

Differentially private algorithms allow large-scale data analytics while preserving user
privacy. Designing such algorithms for graph data is gaining importance with the growth of …

Differentially private algorithms protect individuals in data analysis scenarios by ensuring
that there is only a weak correlation between the existence of the user in the data and the …

Differentially private statistical estimation has seen a flurry of developments over the last
several years. Study has been divided into two schools of thought, focusing on empirical …

In this work, we study the applications of differential privacy (DP) in the context of graph-
structured data. We discuss the formulations of DP applicable to the publication of graphs …

Releasing all pairwise shortest path (APSP) distances between vertices on general graphs
under weight Differential Privacy (DP) is known as a challenging task. In previous work, to …

The goal of privacy-preserving graph publishing is to protect individual privacy in released
graph data while preserving data utility. Degree distribution, serving as fundamental …