A Linear-Time Approximation Scheme for the Densest Subgraph Problem

2026-08-11

AI Summary

This work addresses the densest subgraph problem in undirected graphs—maximizing the ratio of edges to vertices—and presents the first truly linear-time $(1-\varepsilon)$-approximation algorithm, running in $O\left((n+m)/\varepsilon^3 \cdot \log(1/\varepsilon)\right)$ time. The approach integrates flow-based graph weighting, a structural decomposition lemma, and approximation-preserving reductions, iteratively pruning sparse regions and recomputing on progressively smaller subgraphs to efficiently converge to a near-optimal solution. When $\varepsilon$ is constant, the runtime is linear in the graph size. Furthermore, for the variant requiring at least $k$ vertices, the algorithm achieves a $(1/2 - \varepsilon)$-approximation, nearly matching the known $1/2$ hardness barrier for this constrained setting.

Abstract

In the undirected \emph{Densest Subgraph Problem (DSG)} the goal is to output a subset $S$ of vertices of a given graph $G$ that maximizes the quantity $|E(S)|/|S|$, where $E(S)$ is the set of edges in the subgraph induced by $S$. The problem is well studied in both theory and practice, and it admits natural efficient exact algorithms, as well as near-linear time algorithms with a $(1-\varepsilon)$ approximation ratio. However, all previously-known approximation schemes incur logarithmic factors in the size of the graph or other parameters of the graph. This raises the question of whether a linear time $(1-\varepsilon)$-approximation can be obtained for all $\varepsilon>0$. We answer this question affirmatively by providing a $(1-\varepsilon)$-approximation algorithm running in time $O\left(\frac{n+m}{\varepsilon^3}\log \frac{1}{\varepsilon}\right)$, where $m$ and $n$ are respectively the number of edges and vertices of $G$. To the best of our knowledge, this is the first truly linear-time approximation scheme for the problem (when $\varepsilon>0$ is a constant). Our algorithm uses assignments arising from a flow-based formulation together with a structural carving lemma. This lemma allows us to progressively carve "sparse" parts of the graph while nearly preserving the densest subgraph, allowing us to shift heavy computations to smaller instances, which eventually yields the mentioned runtime. Our framework also yields a $(1/2 -\varepsilon)$-approximation for the \emph{Densest At-Least-$k$ Subgraph Problem}, where in addition to maximizing the density, we require the subgraph to have at least $k$ vertices. Our algorithm runs in time $O\left( \frac{(n+m) \log^2 n \log \frac{1}{\varepsilon}}{\varepsilon} \right)$. This nearly matches the known $1/2$ approximation hardness while running in near-linear time.

Problem

• Densest Subgraph Problem
• approximation algorithm
• linear-time
• graph density
• Densest At-Least-k Subgraph Problem

Innovation

• linear-time approximation
• densest subgraph
• structural carving lemma
• flow-based formulation
• near-linear algorithm