Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Maximum Likelihood Estimation of Sparse Networks with Missing
  Observations   Estimating the matrix of connections probabilities is one of the key
questions when studying sparse networks. In this work, we consider networks
generated under the sparse graphon model and the in-homogeneous random graph
model with missing observations. Using the Stochastic Block Model as a
parametric proxy, we bound the risk of the maximum likelihood estimator of
network connections probabilities , and show that it is minimax optimal. When
risk is measured in Frobenius norm, no estimator running in polynomial time has
been shown to attain the minimax optimal rate of convergence for this problem.
Thus, maximum likelihood estimation is of particular interest as
computationally efficient approximations to it have been proposed in the
literature and are often used in practice.

20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci