Metric Embedding Initialization-Based Differentially Private and Explainable Graph Clustering
This work addresses privacy-preserving graph clustering for sensitive data applications, representing an incremental improvement over existing methods.
The paper tackles the problem of differentially private graph clustering by proposing a metric embedding initialization approach that addresses high noise, low efficiency, and poor interpretability issues, demonstrating superior performance on public datasets while ensuring strict privacy.
Graph clustering under the framework of differential privacy, which aims to process graph-structured data while protecting individual privacy, has been receiving increasing attention. Despite significant achievements in current research, challenges such as high noise, low efficiency and poor interpretability continue to severely constrain the development of this field. In this paper, we construct a differentially private and interpretable graph clustering approach based on metric embedding initialization. Specifically, we construct an SDP optimization, extract the key set and provide a well-initialized clustering configuration using an HST-based initialization method. Subsequently, we apply an established k-median clustering strategy to derive the cluster results and offer comparative explanations for the query set through differences from the cluster centers. Extensive experiments on public datasets demonstrate that our proposed framework outperforms existing methods in various clustering metrics while strictly ensuring privacy.