A Fast and Provable Method for Estimating Clique Counts Using Tur\'an's Theorem
Shweta Jain, C. Seshadhri

TL;DR
This paper introduces a fast, provable randomized algorithm for estimating the number of k-cliques in large graphs, leveraging Turán's theorem and a new combinatorial structure called Turán shadow, achieving high accuracy and efficiency.
Contribution
The authors develop a novel algorithm based on Turán's theorem for approximating k-clique counts, scalable to large graphs and larger clique sizes, with practical heuristic implementation.
Findings
Less than 2% error in clique count estimates
Estimates up to size 10 cliques in large social networks within hours
Outperforms existing sampling algorithms in speed and accuracy
Abstract
Clique counts reveal important properties about the structure of massive graphs, especially social networks. The simple setting of just 3-cliques (triangles) has received much attention from the research community. For larger cliques (even, say 6-cliques) the problem quickly becomes intractable because of combinatorial explosion. Most methods used for triangle counting do not scale for large cliques, and existing algorithms require massive parallelism to be feasible. We present a new randomized algorithm that provably approximates the number of k-cliques, for any constant k. The key insight is the use of (strengthenings of) the classic Tur\'an's theorem: this claims that if the edge density of a graph is sufficiently high, the k-clique density must be non-trivial. We define a combinatorial structure called a Tur\'an shadow, the construction of which leads to fast algorithms for clique…
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Taxonomy
TopicsComplex Network Analysis Techniques · Graph Theory and Algorithms · Complexity and Algorithms in Graphs
