Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Fast Sketch Method for Mining User Similarities over Fully Dynamic
  Graph Streams   Many real-world networks such as Twitter and YouTube are given as fully
dynamic graph streams represented as sequences of edge insertions and
deletions. (e.g., users can subscribe and unsubscribe to channels on YouTube).
Existing similarity estimation methods such as MinHash and OPH are customized
to static graphs. We observe that they are indeed sampling methods and exhibit
a sampling bias when applied to fully dynamic graph streams, which results in
large estimation errors. To solve this challenge, we develop a fast and
accurate sketch method VOS. VOS processes each edge in the graph stream of
interest with small time complexity O(1) and uses small memory space to build a
compact sketch of the dynamic graph stream over time. Based on the sketch built
on-the-fly, we develop a method to estimate user similarities over time. We
conduct extensive experiments and the experimental results demonstrate the
efficiency and efficacy of our method.

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The categories of this paper are: cond-mat
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
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The categories of this paper are: atom-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: cmp-lg
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The categories of this paper are: econ
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The categories of this paper are: hep-ex
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th
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The categories of this paper are: adap-org
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The categories of this paper are: ao-sci
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The categories of this paper are: supr-con