Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Unbiased Symmetric Matrix Estimator for Topology Inference under
  Partial Observability   Network topology inference is a fundamental problem in many applications of
network science, such as locating the source of fake news, brain connectivity
networks detection, etc. Many real-world situations suffer from a critical
problem that only a limited part of observations are available. This letter
considers the problem of network topology inference under the framework of
partial observability. Based on the vector autoregressive model, we propose a
novel unbiased estimator for the symmetric network topology with the Gaussian
noise and the Laplacian combination rule. Theoretically, we prove that it
converges to the network combination matrix in probability. Furthermore, by
utilizing the Gaussian mixture model algorithm, an effective algorithm called
network inference Gauss algorithm is developed to infer the network structure.
Finally, compared with the state-of-the-art methods, numerical experiments
demonstrate the proposed algorithm enjoys better performance in the case of
small sample sizes.

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The categories of this paper are: cond-mat
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The categories of this paper are: dg-ga
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The categories of this paper are: q-bio
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The categories of this paper are: nlin
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The categories of this paper are: math
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
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The categories of this paper are: q-alg
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The categories of this paper are: supr-con
1
The categories of this paper are: cs
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin
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The categories of this paper are: atom-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: solv-int