Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Network Sampling Based on NN Representatives   The amount of large-scale real data around us increase in size very quickly
and so does the necessity to reduce its size by obtaining a representative
sample. Such sample allows us to use a great variety of analytical methods,
whose direct application on original data would be infeasible. There are many
methods used for different purposes and with different results. In this paper
we outline a simple and straightforward approach based on analyzing the nearest
neighbors (NN) that is generally applicable. This feature is illustrated on
experiments with weighted networks and vector data. The properties of the
representative sample show that the presented approach maintains very well
internal data structures (e.g. clusters and density). Key technical parameters
of the approach is low complexity and high scalability. This allows the
application of this approach to the area of big data.

12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg