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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 objectmore »

Consider an algorithm performing a computation on a huge random object. Is it necessary to generate the entire object up front, or is it possible to provide query access to the object and sample it incrementally "onthefly"? Such an implementation should emulate the object by answering queries in a manner consistent with a random instance sampled from the true distribution. Our first set of results focus on undirected graphs with independent edge probabilities, under certain assumptions. Then, we use this to obtain the first efficient implementations for the ErdosRenyi model and the Stochastic Block model. As in previous localaccess implementationsmore »

A graph spanner is a fundamental graph structure that faithfully preserves the pairwise distances in the input graph up to a small multiplicative stretch. The common objective in the computation of spanners is to achieve the bestknown existential sizestretch tradeoff efficiently. Classical models and algorithmic analysis of graph spanners essentially assume that the algorithm can read the input graph, construct the desired spanner, and write the answer to the output tape. However, when considering massive graphs containing millions or even billions of nodes not only the input graph, but also the output spanner might be too large for a singlemore »

In this work, we consider the sample complexity required for testing the monotonicity of distributions over partial orders. A distribution p over a poset is {\em monotone} if, for any pair of domain elements x and y such that x⪯y, p(x)≤p(y). To understand the sample complexity of this problem, we introduce a new property called \emph{bigness} over a finite domain, where the distribution is Tbig if the minimum probability for any domain element is at least T. We establish a lower bound of Ω(n/logn) for testing bigness of distributions on domains of size n. We then build on these lowermore »

We study the classic set cover problem from the perspective of sublinear algorithms. Given access to a collection of m sets over n elements in the query model, we show that sublinear algorithms derived from existing techniques have almost tight query complexities. On one hand, first we show an adaptation of the streaming algorithm presented in [17] to the sublinear query model, that returns an αapproximate cover using Õ(m(n/k)^1/(α–1) + nk) queries to the input, where k denotes the value of a minimum set cover. We then complement this upper bound by proving that for lower values of k, themore »

We study the problem of estimating the value of sums of the form Sp≜∑(xip) when one has the ability to sample xi≥0 with probability proportional to its magnitude. When p=2, this problem is equivalent to estimating the selectivity of a selfjoin query in database systems when one can sample rows randomly. We also study the special case when {xi} is the degree sequence of a graph, which corresponds to counting the number of pstars in a graph when one has the ability to sample edges randomly. Our algorithm for a (1±ε)multiplicative approximation of Sp has query and time complexities O(mloglognϵ2S1/pp).more »

In the model of local computation algorithms (LCAs), we aim to compute the queried part of the output by examining only a small (sublinear) portion of the input. Many recently developed LCAs on graph problems achieve time and space complexities with very low dependence on n, the number of vertices. Nonetheless, these complexities are generally at least exponential in d, the upper bound on the degree of the input graph. Instead, we consider the case where parameter d can be moderately dependent on n, and aim for complexities with subexponential dependence on d, while maintaining polylogarithmic dependence on n. Wemore »