Graph sampling by lagged random walk
Statistics Theory
2022-05-16 v1 Statistics Theory
Abstract
We propose a family of lagged random walk sampling methods in simple undirected graphs, where transition to the next state (i.e. node) depends on both the current and previous states -- hence, lagged. The existing random walk sampling methods can be incorporated as special cases. We develop a novel approach to estimation based on lagged random walks at equilibrium, where the target parameter can be any function of values associated with finite-order subgraphs, such as edge, triangle, 4-cycle and others.
Cite
@article{arxiv.2110.03459,
title = {Graph sampling by lagged random walk},
author = {Li-Chun Zhang},
journal= {arXiv preprint arXiv:2110.03459},
year = {2022}
}