TY - JOUR
T1 - Approximating Average Parameters of Graphs
T2 - Sublinear Algorithms 2005
AU - Goldreich, Oded
AU - Ron, Dana
N1 - Publisher Copyright:
© Sublinear Algorithms 2005. All Rights Reserved.
PY - 2006
Y1 - 2006
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 ithneighbor 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 ithneighbor 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 - Sublinear-time algorithms
KW - randomized approximation algorithms
UR - http://www.scopus.com/inward/record.url?scp=85174975681&partnerID=8YFLogxK
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AN - SCOPUS:85174975681
SN - 1862-4405
VL - 5291
JO - Dagstuhl Seminar Proceedings
JF - Dagstuhl Seminar Proceedings
Y2 - 17 July 2005 through 22 July 2005
ER -