Edge Estimation with Independent Set Oracles

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Abstract
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We study the task of estimating the number of edges in a graph, where the access to the graph is provided via an independent set oracle. Independent set queries draw motivation from group testing and have applications to the complexity of decision versus counting problems. We give two algorithms to estimate the number of edges in an n -vertex graph, using (i) polylog( n ) bipartite independent set queries or (ii) n 2/3 polylog( n ) independent set queries.

Highlights

  • We study the problem of estimating the number of edges in a simple, unweighted, undirected graph G = ([n], E), where [n] := {1, 2, . . . , n} and m = |E|, using only an oracle that answers independent set queries

  • Previous work on graph parameter estimation has primarily focused on local queries, such as degree queries, edge existence queries (which answer whether a pair (u, v) forms an edge), or neighbor queries

  • We studied the task of using either Bipartite independent set (BIS) or Independent set (IS) queries to estimate the number of edges in a graph

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Summary

IntroductionExpand/Collapse icon

Previous work on graph parameter estimation has primarily focused on local queries, such as degree queries (which output the degree of a vertex v), edge existence queries (which answer whether a pair (u, v) forms an edge), or neighbor queries (which provide the ith neighbor of a vertex v). Such queries cannot achieve sub-polynomial query costs on certain lower bound graphs identified by Feige [13] and Goldreich and Ron [15], essentially due to the fact that these queries can only obtain local information about the graph. The independent set queries described above naturally generalize an edge existence query, and their non-locality opens the door for sub-polynomial query algorithms for various graph parameter estimation tasks

Motivation and Related WorkExpand/Collapse icon
Our ResultsExpand/Collapse icon
BIS AlgorithmExpand/Collapse icon
Exact CountingExpand/Collapse icon
Coarse EstimatorExpand/Collapse icon
IS AlgorithmExpand/Collapse icon
OutlineExpand/Collapse icon
Exact Edge Counting using BIS and IS QueriesExpand/Collapse icon
Proof of Lemma 5Expand/Collapse icon
Sparsification by ColoringExpand/Collapse icon
Edge Estimation using IS QueriesExpand/Collapse icon
ConclusionExpand/Collapse icon
Open DirectionsExpand/Collapse icon
A Concentration BoundsExpand/Collapse icon
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We study the complexity of determining the edge connectivity of a simple graph with cut queries. We show that (i) there is a bounded-error randomized algorithm that computes edge connectivity with $O(n)$ cut queries, and (ii) there is a bounded-error quantum algorithm that computes edge connectivity with $\tilde{O}(\sqrt{}$n) cut queries. To prove these results we introduce a new technique, called star contraction, to randomly contract edges of a graph while preserving non-trivial minimum cuts. In star contraction vertices randomly contract an edge incident on a small set of randomly chosen “center” vertices. In contrast to the related 2-out contraction technique of Ghaffari, Nowicki, and Thorup [SODA’20], star contraction only contracts vertex-disjoint star subgraphs, which allows it to be efficiently implemented via cut queries. The $O(n)$ bound from item (i) was not known even for the simpler problem of connectivity, and it improves the $O(n\log^{3}n)$ upper bound by Rubinstein, Schramm, and Weinberg [ITCS’18]. The bound is tight under the reasonable conjecture that the randomized communication complexity of connectivity is $\Omega(n\log n)$, an open question since the seminal work of Babai, Frankl, and Simon [FOCS’86]. The bound also excludes using edge connectivity on simple graphs to prove a superlinear randomized query lower bound for minimizing a symmetric submodular function. The quantum algorithm from item (ii) gives a nearlyquadratic separation with the randomized complexity, and addresses an open question of Lee, Santha, and Zhang [SODA’21]. The algorithm can alternatively be viewed as computing the edge connectivity of a simple graph with $\tilde{O}(\sqrt{}$n) matrix-vector multiplication queries to its adjacency matrix. Finally, we demonstrate the use of star contraction outside of the cut query setting by designing a one-pass semi-streaming algorithm for computing edge connectivity in the complete vertex arrival setting. This contrasts with the edge arrival setting where two passes are required.

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In this paper, we design efficient algorithms to approximately count the number of edges of a given $k$-hypergraph, and to sample an approximately uniform random edge. The hypergraph is not given explicitly and can be accessed only through its colorful independence oracle: The colorful independence oracle returns yes or no depending on whether a given subset of the vertices contains an edge that is colorful with respect to a given vertex-coloring. Our results extend and/or strengthen recent results in the graph oracle literature due to Beame et al. [ACM Trans. Algorithms, 16 (2020), 52], Dell and Lapinskas [Proceedings of STOC, ACM, 2018, pp. 281--288], and Bhattacharya et al. [Proceedings of ISAAC, 2019]. Our results have consequences for approximate counting/sampling: We can turn certain kinds of decision algorithms into approximate counting/sampling algorithms without causing much overhead in the running time. We apply this approximate counting/sampling-to-decision reduction to key problems in fine-grained complexity (such as $k$-SUM, $k$-OV, and weighted $k$-Clique) and parameterized complexity (such as induced subgraphs of size $k$ or weight-$k$ solutions to constraint satisfaction problems).

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