Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Connectedness Constraint for Learning Sparse Graphs   Graphs are naturally sparse objects that are used to study many problems
involving networks, for example, distributed learning and graph signal
processing. In some cases, the graph is not given, but must be learned from the
problem and available data. Often it is desirable to learn sparse graphs.
However, making a graph highly sparse can split the graph into several
disconnected components, leading to several separate networks. The main
difficulty is that connectedness is often treated as a combinatorial property,
making it hard to enforce in e.g. convex optimization problems. In this
article, we show how connectedness of undirected graphs can be formulated as an
analytical property and can be enforced as a convex constraint. We especially
show how the constraint relates to the distributed consensus problem and graph
Laplacian learning. Using simulated and real data, we perform experiments to
learn sparse and connected graphs from data.

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The categories of this paper are: dg-ga
1
The categories of this paper are: cs
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom