Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Matched Filters for Noisy Induced Subgraph Detection   The problem of finding the vertex correspondence between two noisy graphs
with different number of vertices where the smaller graph is still large has
many applications in social networks, neuroscience, and computer vision. We
propose a solution to this problem via a graph matching matched filter:
centering and padding the smaller adjacency matrix and applying graph matching
methods to align it to the larger network. The centering and padding schemes
can be incorporated into any algorithm that matches using adjacency matrices.
Under a statistical model for correlated pairs of graphs, which yields a noisy
copy of the small graph within the larger graph, the resulting optimization
problem can be guaranteed to recover the true vertex correspondence between the
networks.
  However, there are currently no efficient algorithms for solving this
problem. To illustrate the possibilities and challenges of such problems, we
use an algorithm that can exploit a partially known correspondence and show via
varied simulations and applications to {\it Drosophila} and human connectomes
that this approach can achieve good performance.

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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-ex
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The categories of this paper are: math-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: q-alg
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The categories of this paper are: comp-gas
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The categories of this paper are: gr-qc
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The categories of this paper are: eess
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-ph
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The categories of this paper are: solv-int
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-lat