TY - JOUR

T1 - Approximating average parameters of graphs

AU - Goldreich, Oded

AU - Ron, Dana

PY - 2008/7

Y1 - 2008/7

N2 - Inspired by Feige (36th STOC, 2004), we initiate a study of sublinear randomized algorithms for approximating average parameters of a graph. Specifically, we consider the average degree of a graph and the average distance between pairs of vertices in a graph. Since our focus is on sublinear algorithms, these algorithms access the input graph via queries to an adequate oracle. We consider two types of queries. The first type is standard neighborhood queries (i.e., what is the ith neighbor of vertex v?), whereas the second type are queries regarding the quantities that we need to find the average of (i.e., what is the degree of vertex v? and what is the distance between u and v?, respectively). Loosely speaking, our results indicate a difference between the two problems: For approximating the average degree, the standard neighbor queries suffice and in fact are preferable to degree queries. In contrast, for approximating average distances, the standard neighbor queries are of little help whereas distance queries are crucial.

AB - Inspired by Feige (36th STOC, 2004), we initiate a study of sublinear randomized algorithms for approximating average parameters of a graph. Specifically, we consider the average degree of a graph and the average distance between pairs of vertices in a graph. Since our focus is on sublinear algorithms, these algorithms access the input graph via queries to an adequate oracle. We consider two types of queries. The first type is standard neighborhood queries (i.e., what is the ith neighbor of vertex v?), whereas the second type are queries regarding the quantities that we need to find the average of (i.e., what is the degree of vertex v? and what is the distance between u and v?, respectively). Loosely speaking, our results indicate a difference between the two problems: For approximating the average degree, the standard neighbor queries suffice and in fact are preferable to degree queries. In contrast, for approximating average distances, the standard neighbor queries are of little help whereas distance queries are crucial.

KW - Randomized approximation algorithms

KW - Sublinear-time algorithms

KW - Wiener index

UR - http://www.scopus.com/inward/record.url?scp=52349103789&partnerID=8YFLogxK

U2 - 10.1002/rsa.20203

DO - 10.1002/rsa.20203

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AN - SCOPUS:52349103789

SN - 1042-9832

VL - 32

SP - 473

EP - 493

JO - Random Structures and Algorithms

JF - Random Structures and Algorithms

IS - 4

ER -