Improved mixing time for k-subgraph sampling*

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A4 Artikkeli konferenssijulkaisussa
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Author
Date
2020
Department
Tokyo Institute of Technology
Adj. Prof. Gionis Aris group
Department of Computer Science
Major/Subject
Mcode
Degree programme
Language
en
Pages
9
568-576
Series
Proceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020
Abstract
Understanding the local structure of a graph provides valuable insights about the underlying phenomena from which the graph has originated. Sampling and examining k-subgraphs is a widely used approach to understand the local structure of a graph. In this paper, we study the problem of sampling uniformly k-subgraphs from a given graph. We analyse a few different Markov chain Monte Carlo (MCMC) approaches, and obtain analytical results on their mixing times, which improve significantly the state of the art. In particular, we improve the bound on the mixing times of the standard MCMC approach, and the state-of-the-art MCMC sampling method PSRW, using the canonical-paths argument. In addition, we propose a novel sampling method, which we call recursive subgraph sampling RSS, and its optimized variant RSS+. The proposed methods, RSS and RSS+, are provably faster than the existing approaches. We conduct experiments and verify the uniformity of samples and the efficiency of RSS and RSS+.
Description
| openaire: EC/H2020/654024/EU//SoBigData
Keywords
DOI
10.1137/1.9781611976236.64
Citation
Matsuno , R & Gionis , A 2020 , Improved mixing time for k-subgraph sampling* . in C Demeniconi & N Chawla (eds) , Proceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020 . Society for Industrial and Applied Mathematics (SIAM) , pp. 568-576 , SIAM International Conference on Data Mining , Cincinnati , Ohio , United States , 07/05/2020 . https://doi.org/10.1137/1.9781611976236.64
Permanent link to this item
https://urn.fi/URN:NBN:fi:aalto-202102262093
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[article-cris] Perustieteiden korkeakoulu / SCI