Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CSNE: Conditional Signed Network Embedding   Signed networks are mathematical structures that encode positive and negative
relations between entities such as friend/foe or trust/distrust. Recently,
several papers studied the construction of useful low-dimensional
representations (embeddings) of these networks for the prediction of missing
relations or signs. Existing embedding methods for sign prediction generally
enforce different notions of status or balance theories in their optimization
function. These theories, however, are often inaccurate or incomplete, which
negatively impacts method performance.
  In this context, we introduce conditional signed network embedding (CSNE).
Our probabilistic approach models structural information about the signs in the
network separately from fine-grained detail. Structural information is
represented in the form of a prior, while the embedding itself is used for
capturing fine-grained information. These components are then integrated in a
rigorous manner. CSNE's accuracy depends on the existence of sufficiently
powerful structural priors for modelling signed networks, currently unavailable
in the literature. Thus, as a second main contribution, which we find to be
highly valuable in its own right, we also introduce a novel approach to
construct priors based on the Maximum Entropy (MaxEnt) principle. These priors
can model the \emph{polarity} of nodes (degree to which their links are
positive) as well as signed \emph{triangle counts} (a measure of the degree
structural balance holds to in a network).
  Experiments on a variety of real-world networks confirm that CSNE outperforms
the state-of-the-art on the task of sign prediction. Moreover, the MaxEnt
priors on their own, while less accurate than full CSNE, achieve accuracies
competitive with the state-of-the-art at very limited computational cost, thus
providing an excellent runtime-accuracy trade-off in resource-constrained
situations.

5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an