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#### The Arboricity Captures the Complexity of Sampling Edges

##### MPS-Authors
/persons/resource/persons230547

Rosenbaum,  Will
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

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##### Fulltext (public)

arXiv:1902.08086.pdf
(Preprint), 359KB

##### Supplementary Material (public)
There is no public supplementary material available
##### Citation

Eden, T., Ron, D., & Rosenbaum, W. (2019). The Arboricity Captures the Complexity of Sampling Edges. Retrieved from http://arxiv.org/abs/1902.08086.

Cite as: http://hdl.handle.net/21.11116/0000-0003-0CD0-5
##### Abstract
In this paper, we revisit the problem of sampling edges in an unknown graph $G = (V, E)$ from a distribution that is (pointwise) almost uniform over $E$. We consider the case where there is some a priori upper bound on the arboriciy of $G$. Given query access to a graph $G$ over $n$ vertices and of average degree $d$ and arboricity at most $\alpha$, we design an algorithm that performs $O\!\left(\frac{\alpha}{d} \cdot \frac{\log^3 n}{\varepsilon}\right)$ queries in expectation and returns an edge in the graph such that every edge $e \in E$ is sampled with probability $(1 \pm \varepsilon)/m$. The algorithm performs two types of queries: degree queries and neighbor queries. We show that the upper bound is tight (up to poly-logarithmic factors and the dependence in $\varepsilon$), as $\Omega\!\left(\frac{\alpha}{d} \right)$ queries are necessary for the easier task of sampling edges from any distribution over $E$ that is close to uniform in total variational distance. We also prove that even if $G$ is a tree (i.e., $\alpha = 1$ so that $\frac{\alpha}{d}=\Theta(1)$), $\Omega\left(\frac{\log n}{\log\log n}\right)$ queries are necessary to sample an edge from any distribution that is pointwise close to uniform, thus establishing that a $\mathrm{poly}(\log n)$ factor is necessary for constant $\alpha$. Finally we show how our algorithm can be applied to obtain a new result on approximately counting subgraphs, based on the recent work of Assadi, Kapralov, and Khanna (ITCS, 2019).