Biswas, Amartya Shankha ;
Eden, Talya ;
Liu, Quanquan C. ;
Rubinfeld, Ronitt ;
Mitrović, Slobodan
Massively Parallel Algorithms for Small Subgraph Counting
Abstract
Over the last two decades, frameworks for distributedmemory parallel computation, such as MapReduce, Hadoop, Spark and Dryad, have gained significant popularity with the growing prevalence of large network datasets. The Massively Parallel Computation (MPC) model is the defacto standard for studying graph algorithms in these frameworks theoretically. Subgraph counting is one such fundamental problem in analyzing massive graphs, with the main algorithmic challenges centering on designing methods which are both scalable and accurate.
Given a graph G = (V, E) with n vertices, m edges and T triangles, our first result is an algorithm that outputs a (1+ε)approximation to T, with asymptotically optimal round and total space complexity provided any S ≥ max{(√ m, n²/m)} space per machine and assuming T = Ω(√{m/n}). Our result gives a quadratic improvement on the bound on T over previous works. We also provide a simple extension of our result to counting any subgraph of k size for constant k ≥ 1. Our second result is an O_δ(log log n)round algorithm for exactly counting the number of triangles, whose total space usage is parametrized by the arboricity α of the input graph. We extend this result to exactly counting kcliques for any constant k. Finally, we prove that a recent result of Bera, Pashanasangi and Seshadhri (ITCS 2020) for exactly counting all subgraphs of size at most 5 can be implemented in the MPC model in Õ_δ(√{log n}) rounds, O(n^δ) space per machine and O(mα³) total space.
In addition to our theoretical results, we simulate our triangle counting algorithms in realworld graphs obtained from the Stanford Network Analysis Project (SNAP) database. Our results show that both our approximate and exact counting algorithms exhibit improvements in terms of round complexity and approximation ratio, respectively, compared to two previous widely used algorithms for these problems.
BibTeX  Entry
@InProceedings{biswas_et_al:LIPIcs.APPROX/RANDOM.2022.39,
author = {Biswas, Amartya Shankha and Eden, Talya and Liu, Quanquan C. and Rubinfeld, Ronitt and Mitrovi\'{c}, Slobodan},
title = {{Massively Parallel Algorithms for Small Subgraph Counting}},
booktitle = {Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2022)},
pages = {39:139:28},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {9783959772495},
ISSN = {18688969},
year = {2022},
volume = {245},
editor = {Chakrabarti, Amit and Swamy, Chaitanya},
publisher = {Schloss Dagstuhl  LeibnizZentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2022/17161},
URN = {urn:nbn:de:0030drops171619},
doi = {10.4230/LIPIcs.APPROX/RANDOM.2022.39},
annote = {Keywords: triangle counting, massively parallel computation, clique counting, approximation algorithms, subgraph counting}
}
15.09.2022
Keywords: 

triangle counting, massively parallel computation, clique counting, approximation algorithms, subgraph counting 
Seminar: 

Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2022)

Issue date: 

2022 
Date of publication: 

15.09.2022 