Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Discovering Key Nodes in a Temporal Social Network   [Background]Discovering key nodes plays a significant role in Social Network
Analysis(SNA). Effective and accurate mining of key nodes promotes more
successful applications in fields like advertisement and recommendation.
[Methods] With focus on the temporal and categorical property of users' actions
- when did they re-tweet or reply a message, as well as their social intimacy
measured by structural embeddings, we designed a more sensitive PageRank-like
algorithm to accommodate the growing and changing social network in the pursue
of mining key nodes. [Results] Compared with our baseline PageRank algorithm,
key nodes selected by our ranking algorithm noticeably perform better in the
SIR disease simulations with SNAP Higgs dataset. [Conclusion] These results
contributed to a better understanding of disseminations of social events over
the network.

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The categories of this paper are: astro-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: math
34
The categories of this paper are: chem-ph
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The categories of this paper are: stat
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The categories of this paper are: econ
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The categories of this paper are: q-alg
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The categories of this paper are: hep-ex
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The categories of this paper are: bayes-an
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The categories of this paper are: cond-mat
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The categories of this paper are: funct-an
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The categories of this paper are: alg-geom
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The categories of this paper are: acc-phys
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The categories of this paper are: cmp-lg
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The categories of this paper are: ao-sci
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The categories of this paper are: dg-ga