Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Systematic identification of statistically significant network measures   We present a novel graph embedding space (i.e., a set of measures on graphs)
for performing statistical analyses of networks. Key improvements over existing
approaches include discovery of "motif-hubs" (multiple overlapping significant
subgraphs), computational efficiency relative to subgraph census, and
flexibility (the method is easily generalizable to weighted and signed graphs).
The embedding space is based on {\it scalars}, functionals of the adjacency
matrix representing the network. {\it Scalars} are global, involving all nodes;
although they can be related to subgraph enumeration, there is not a one-to-one
mapping between scalars and subgraphs. Improvements in network randomization
and significance testing--we learn the distribution rather than assuming
gaussianity--are also presented. The resulting algorithm establishes a
systematic approach to the identification of the most significant scalars and
suggests machine-learning techniques for network classification.

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The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
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The categories of this paper are: atom-ph
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The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
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The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th