English

Finding Dense Subgraphs in G(n,1/2)

Data Structures and Algorithms 2008-09-22 v2

Abstract

Finding the largest clique is a notoriously hard problem, even on random graphs. It is known that the clique number of a random graph G(n,1/2) is almost surely either k or k+1, where k = 2log n - 2log(log n) - 1. However, a simple greedy algorithm finds a clique of size only (1+o(1))log n, with high probability, and finding larger cliques -- that of size even (1+ epsilon)log n -- in randomized polynomial time has been a long-standing open problem. In this paper, we study the following generalization: given a random graph G(n,1/2), find the largest subgraph with edge density at least (1-delta). We show that a simple modification of the greedy algorithm finds a subset of 2log n vertices whose induced subgraph has edge density at least 0.951, with high probability. To complement this, we show that almost surely there is no subset of 2.784log n vertices whose induced subgraph has edge density 0.951 or more.

Keywords

Cite

@article{arxiv.0807.5111,
  title  = {Finding Dense Subgraphs in G(n,1/2)},
  author = {Atish Das Sarma and Amit Deshpande and Ravi Kannan},
  journal= {arXiv preprint arXiv:0807.5111},
  year   = {2008}
}

Comments

6 pages

R2 v1 2026-06-21T11:06:25.564Z