TY - GEN
T1 - Distribution and dependence of extremes in network sampling processes
AU - Avrachenkov, Konstantin
AU - Markovich, Natalia M.
AU - Sreedharan, Jithin K.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - We explore the dependence structure in the sampled sequence of complex networks. We consider randomized algorithms to sample the nodes and study external properties in any associated stationary sequence of characteristics of interest like node degrees, number of followers or income of the nodes in Online Social Networks etc, which satisfy two mixing conditions. Several useful extremes of the sampled sequence like kth largest value, clusters of exceedances over a threshold, first hitting time of a large value etc are investigated. We abstract the dependence and the statistics of extremes into a single parameter that appears in Extreme Value Theory, called external index (EI). In this work, we derive this parameter analytically and also estimate it empirically. We propose the use of EI as a parameter to compare different sampling procedures. As a specific example, degree correlations between neighboring nodes are studied in detail with three prominent random walks as sampling techniques.
AB - We explore the dependence structure in the sampled sequence of complex networks. We consider randomized algorithms to sample the nodes and study external properties in any associated stationary sequence of characteristics of interest like node degrees, number of followers or income of the nodes in Online Social Networks etc, which satisfy two mixing conditions. Several useful extremes of the sampled sequence like kth largest value, clusters of exceedances over a threshold, first hitting time of a large value etc are investigated. We abstract the dependence and the statistics of extremes into a single parameter that appears in Extreme Value Theory, called external index (EI). In this work, we derive this parameter analytically and also estimate it empirically. We propose the use of EI as a parameter to compare different sampling procedures. As a specific example, degree correlations between neighboring nodes are studied in detail with three prominent random walks as sampling techniques.
KW - Network sampling
KW - extremal index
KW - extreme value theory
KW - random walks on graph
UR - https://www.scopus.com/pages/publications/84928527250
U2 - 10.1109/SITIS.2014.91
DO - 10.1109/SITIS.2014.91
M3 - Conference contribution
AN - SCOPUS:84928527250
T3 - Proceedings - 10th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2014
SP - 331
EP - 338
BT - Proceedings - 10th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2014
A2 - Yetongnon, Kokou
A2 - Dipanda, Albert
A2 - Chbeir, Richard
A2 - Chbeir, Richard
PB - Institute of Electrical and Electronics Engineers
T2 - 10th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2014
Y2 - 23 November 2014 through 27 November 2014
ER -