Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Inference of Network Summary Statistics Through Network Denoising   Consider observing an undirected network that is `noisy' in the sense that
there are Type I and Type II errors in the observation of edges. Such errors
can arise, for example, in the context of inferring gene regulatory networks in
genomics or functional connectivity networks in neuroscience. Given a single
observed network then, to what extent are summary statistics for that network
representative of their analogues for the true underlying network? Can we infer
such statistics more accurately by taking into account the noise in the
observed network edges?
  In this paper, we answer both of these questions. In particular, we develop a
spectral-based methodology using the adjacency matrix to `denoise' the observed
network data and produce more accurate inference of the summary statistics of
the true network. We characterize performance of our methodology through bounds
on appropriate notions of risk in the $L^2$ sense, and conclude by illustrating
the practical impact of this work on synthetic and real-world data.

36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
1
The categories of this paper are: cs
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg