Towards a Decomposition-Optimal Algorithm for Counting and Sampling Arbitrary Motifs in Sublinear Time

Biswas, Amartya Shankha; Eden, Talya; Rubinfeld, Ronitt

doi:10.4230/lipics.approx/random.2021.55

2021

DOI: 10.4230/lipics.approx/random.2021.55

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Towards a Decomposition-Optimal Algorithm for Counting and Sampling Arbitrary Motifs in Sublinear Time

Amartya Shankha Biswas

Talya Eden

Ronitt Rubinfeld

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2023

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Efficient and Near-optimal Algorithms for Sampling Small Connected Subgraphs

Bressan

2023

ACM Trans. Algorithms

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We study the following problem: Given an integer k ≥ 3 and a simple graph G , sample a connected induced k -vertex subgraph of G uniformly at random. This is a fundamental graph mining primitive with applications in social network analysis, bioinformatics, and more. Surprisingly, no efficient algorithm is known for uniform sampling; the only somewhat efficient algorithms available yield samples that are only approximately uniform, with running times that are unclear or suboptimal. In this work, we provide: (i) a near-optimal mixing time bound for a well-known random walk technique, (ii) the first efficient algorithm for truly uniform graphlet sampling, and (iii) the first sublinear-time algorithm for ε-uniform graphlet sampling.

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Efficient and Near-optimal Algorithms for Sampling Small Connected Subgraphs

Bressan

2023

ACM Trans. Algorithms

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Towards a Decomposition-Optimal Algorithm for Counting and Sampling Arbitrary Motifs in Sublinear Time

Cited by 1 publication

References 0 publications

Efficient and Near-optimal Algorithms for Sampling Small Connected Subgraphs

Efficient and Near-optimal Algorithms for Sampling Small Connected Subgraphs

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