Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficiently Estimating Motif Statistics of Large Networks   Exploring statistics of locally connected subgraph patterns (also known as
network motifs) has helped researchers better understand the structure and
function of biological and online social networks (OSNs). Nowadays the massive
size of some critical networks -- often stored in already overloaded relational
databases -- effectively limits the rate at which nodes and edges can be
explored, making it a challenge to accurately discover subgraph statistics. In
this work, we propose sampling methods to accurately estimate subgraph
statistics from as few queried nodes as possible. We present sampling
algorithms that efficiently and accurately estimate subgraph properties of
massive networks. Our algorithms require no pre-computation or complete network
topology information. At the same time, we provide theoretical guarantees of
convergence. We perform experiments using widely known data sets, and show that
for the same accuracy, our algorithms require an order of magnitude less
queries (samples) than the current state-of-the-art algorithms.

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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
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The categories of this paper are: q-fin
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
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The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
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The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con