Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: BIG sampling   Graph sampling is a statistical approach to study real graphs, which
represent the structure of many technological, social or biological phenomena
of interest. We develop bipartite incident graph sampling (BIGS) as a feasible
representation of graph sampling from arbitrary finite graphs. It provides also
a unified treatment of the existing unconventional sampling methods which were
studied separately in the past, including indirect, network and adaptive
cluster sampling. The sufficient and necessary conditions of feasible BIGS
representation are established, given which one can apply a family of
Hansen-Hurwitz type design-unbiased estimators in addition to the standard
Horvitz-Thompson estimator. The approach increases therefore the potentials of
efficiency gains in graph sampling. A general result regarding the relative
efficiency of the two types of estimators is obtained. Numerical examples are
given to illustrate the versatility of the proposed approach.

3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
1
The categories of this paper are: cs
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org