# Differentially Private Densest Subgraph

arXiv: https://arxiv.org/abs/2106.00508 · HTML: https://mlsanity.com/paper/2106.00508/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2021.

## Built on by (most cited first, 6 of 6)

- [Differential Privacy Mechanisms in Neural Tangent Kernel Regression](https://mlsanity.com/paper/2407.13621/) (2024)
- [A Survey on the Densest Subgraph Problem and Its Variants](https://mlsanity.com/paper/2303.14467/) (2023)
- [Near-Optimal Correlation Clustering with Privacy](https://mlsanity.com/paper/2203.01440/) (2022)
- [On the Statistical Complexity of Estimation and Testing under Privacy Constraints](https://mlsanity.com/paper/2210.02215/) (2022)
- [Differentially Private and Scalable Estimation of the Network Principal Component](https://mlsanity.com/paper/2505.03858/) (2025)
- [Differentially Private Spectral Graph Clustering: Balancing Privacy, Accuracy, and Efficiency](https://mlsanity.com/paper/2510.07136/) (2025)

## Abstract (arXiv)

Given a graph, the densest subgraph problem asks for a set of vertices such that the average degree among these vertices is maximized. Densest subgraph has numerous applications in learning, e.g., community detection in social networks, link spam detection, correlation mining, bioinformatics, and so on. Although there are efficient algorithms that output either exact or approximate solutions to the densest subgraph problem, existing algorithms may violate the privacy of the individuals in the network, e.g., leaking the existence/non-existence of edges. In this paper, we study the densest subgraph problem in the framework of the differential privacy, and we derive the first upper and lower bounds for this problem. We show that there exists a linear-time ε-differentially private algorithm that finds a 2-approximation of the densest subgraph with an extra poly-logarithmic additive error. Our algorithm not only reports the approximate density of the densest subgraph, but also reports the vertices that form the dense subgraph. Our upper bound almost matches the famous 2-approximation by Charikar both in performance and in approximation ratio, but we additionally achieve differential privacy. In comparison with Charikar's algorithm, our algorithm has an extra poly-logarithmic additive error. We partly justify the additive error with a new lower bound, showing that for any differentially private algorithm that provides a constant-factor approximation, a sub-logarithmic additive error is inherent. We also practically study our differentially private algorithm on real-world graphs, and we show that in practice the algorithm finds a solution which is very close to the optimal