Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Concentration and consistency results for canonical and curved
  exponential-family models of random graphs   Statistical inference for exponential-family models of random graphs with
dependent edges is challenging. We stress the importance of additional
structure and show that additional structure facilitates statistical inference.
A simple example of a random graph with additional structure is a random graph
with neighborhoods and local dependence within neighborhoods. We develop the
first concentration and consistency results for maximum likelihood and
$M$-estimators of a wide range of canonical and curved exponential-family
models of random graphs with local dependence. All results are non-asymptotic
and applicable to random graphs with finite populations of nodes, although
asymptotic consistency results can be obtained as well. In addition, we show
that additional structure can facilitate subgraph-to-graph estimation, and
present concentration results for subgraph-to-graph estimators. As an
application, we consider popular curved exponential-family models of random
graphs, with local dependence induced by transitivity and parameter vectors
whose dimensions depend on the number of nodes.

24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
1
The categories of this paper are: cs