Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Rank me thou shalln't Compare me   Centrality measures have been defined to quantify the importance of a node in
complex networks. The relative importance of a node can be measured using its
centrality rank based on the centrality value. In the present work, we predict
the degree centrality rank of a node without having the entire network. The
proposed method uses degree of the node and some network parameters to predict
its rank. These network parameters include network size, minimum, maximum, and
average degree of the network. These parameters are estimated using random walk
sampling techniques. The proposed method is validated on Barabasi-Albert
networks. Simulation results show that the proposed method predicts the rank of
higher degree nodes with more accuracy. The average error in the rank
prediction is approximately $0.16\%$ of the network size.

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The categories of this paper are: nucl-th
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The categories of this paper are: solv-int
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: supr-con
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The categories of this paper are: dg-ga
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The categories of this paper are: adap-org
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The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: q-alg
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The categories of this paper are: ao-sci
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The categories of this paper are: cond-mat
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-th