Abstract
Consider an algorithm performing a computation on a huge random object (for example a random graph or a "long" random walk). Is it necessary to generate the entire object prior to the computation, or is it possible to provide query access to the object and sample it incrementally "onthefly" (as requested by the algorithm)? Such an implementation should emulate the random object by answering queries in a manner consistent with an instance of the random object sampled from the true distribution (or close to it). This paradigm is useful when the algorithm is sublinear and thus, sampling the entire object up front would ruin its efficiency.
Our first set of results focus on undirected graphs with independent edge probabilities, i.e. each edge is chosen as an independent Bernoulli random variable. We provide a general implementation for this model under certain assumptions. Then, we use this to obtain the first efficient local implementations for the ErdösRényi G(n,p) model for all values of p, and the Stochastic Block model. As in previous localaccess implementations for random graphs, we support VertexPair and NextNeighbor queries. In addition, we introduce a new RandomNeighbor query. Next, we give the first localaccess implementation for AllNeighbors queries in the (sparse and directed) Kleinberg’s SmallWorld model. Our implementations require no preprocessing time, and answer each query using O(poly(log n)) time, random bits, and additional space.
Next, we show how to implement random Catalan objects, specifically focusing on Dyck paths (balanced random walks on the integer line that are always nonnegative). Here, we support Height queries to find the location of the walk, and FirstReturn queries to find the time when the walk returns to a specified location. This in turn can be used to implement NextNeighbor queries on random rooted ordered trees, and MatchingBracket queries on random well bracketed expressions (the Dyck language).
Finally, we introduce two features to define a new model that: (1) allows multiple independent (and even simultaneous) instantiations of the same implementation, to be consistent with each other without the need for communication, (2) allows us to generate a richer class of random objects that do not have a succinct description. Specifically, we study uniformly random valid qcolorings of an input graph G with maximum degree Δ. This is in contrast to prior work in the area, where the relevant random objects are defined as a distribution with O(1) parameters (for example, n and p in the G(n,p) model). The distribution over valid colorings is instead specified via a "huge" input (the underlying graph G), that is far too large to be read by a sublinear time algorithm. Instead, our implementation accesses G through local neighborhood probes, and is able to answer queries to the color of any given vertex in sublinear time for q ≥ 9Δ, in a manner that is consistent with a specific random valid coloring of G. Furthermore, the implementation is memoryless, and can maintain consistency with noncommunicating copies of itself.
BibTeX  Entry
@InProceedings{biswas_et_al:LIPIcs:2020:11712,
author = {Amartya Shankha Biswas and Ronitt Rubinfeld and Anak Yodpinyanee},
title = {{Local Access to Huge Random Objects Through Partial Sampling}},
booktitle = {11th Innovations in Theoretical Computer Science Conference (ITCS 2020)},
pages = {27:127:65},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {9783959771344},
ISSN = {18688969},
year = {2020},
volume = {151},
editor = {Thomas Vidick},
publisher = {Schloss DagstuhlLeibnizZentrum fuer Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2020/11712},
URN = {urn:nbn:de:0030drops117126},
doi = {10.4230/LIPIcs.ITCS.2020.27},
annote = {Keywords: sublinear time algorithms, random generation, local computation}
}
Keywords: 

sublinear time algorithms, random generation, local computation 
Collection: 

11th Innovations in Theoretical Computer Science Conference (ITCS 2020) 
Issue Date: 

2020 
Date of publication: 

06.01.2020 