Improved Robust Estimation for Erdős-Rényi Graphs: The Sparse Regime and Optimal Breakdown Point

Hongjie Chen, Jingqiu Ding, Yiding Hua, Stefan Tiegel

Keywords: Robust statistics, random graph estimation, sum-of-squares

Abstract:

We study the problem of robustly estimating the edge density of Erdos Renyi random graphs $\mathbb{G}(n, d^\circ/n)$ when an adversary can arbitrarily add or remove edges incident to an $\eta$-fraction of the nodes.
We develop the first polynomial-time algorithm for this problem that estimates $d^\circ$ up to an additive error $O\left({[\sqrt{\log(n) / n} + \eta\sqrt{\log(1/\eta)} ] \cdot \sqrt{d^\circ} + \eta \log(1/\eta)}\right)$.
Our error guarantee matches information-theoretic lower bounds up to factors of $\log(1/\eta)$.
Moreover, our estimator works for all $d^\circ \geq \Omega(1)$ and achieves optimal breakdown point $\eta = 1/2$.

Previous algorithms [Acharya et al 2022, Chen et al 2024], including inefficient ones, incur significantly suboptimal errors.
Furthermore, even admitting suboptimal error guarantees, only inefficient algorithms achieve optimal breakdown point.
Our algorithm is based on the sum-of-squares (SoS) hierarchy.
A key ingredient is to construct constant-degree SoS certificates for concentration of the number of edges incident to small sets in $\mathbb{G}(n, d^\circ/n)$.
Crucially, we show that these certificates also exist in the sparse regime, when $d^\circ = o(\log n)$, a regime in which the performance of previous algorithms was significantly suboptimal.

Primary Area: Theory (e.g., control theory, learning theory, algorithmic game theory)

Submission Number: 10246