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Local Experts Finding Across Multiple Social Networks

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Database Systems for Advanced Applications (DASFAA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11447))

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Abstract

The local experts finding, which aims to identify a set of k people with specialized knowledge around a particular location, has become a hot topic along with the popularity of social networks, such as Twitter, Facebook. Local experts are important for many applications, such as answering local information queries, personalized recommendation. In many real-world applications, complete social information should be collected from multiple social networks, in which people usually participate in and active. However, previous approaches of local experts finding mostly focus on a single social network. In this paper, as far as we know, we are the first to study the local experts finding problem across multiple large social networks. Specifically, we want to identify a set of k people with the highest score, where the score of a person is a combination of local authority and topic knowledge of the person. To efficiently tackle this problem, we propose a novel framework, KTMSNs (knowledge transfer across multiple social networks). KTMSNs consists of two steps. Firstly, given a person over multiple social networks, we calculate the local authority and the topic knowledge, respectively. We propose a social topology-aware inverted index to speed up the calculation of the two values. Secondly, we propose a skyline-based strategy to combine the two values for obtaining the score of a person. Experimental studies on real social network datasets demonstrate the efficiency and effectiveness of our proposed approach.

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Notes

  1. 1.

    https://en.wikipedia.org/wiki/Portal:Contents/Portals.

  2. 2.

    https://developer.foursquare.com/.

  3. 3.

    https://dev.twitter.com/.

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Acknowledgments

This research is partially funded by the National Key Research and Development Program of China (Grant No. 2016YFC1401900), the National Natural Science Foundation of China (Grant Nos. 61572119, 61572121, 61622202, 61732003, 61729201, 61702086, and U1401256), the Fundamental Research Funds for the Central Universities (Grant Nos. N171604007, and N171904007), the Natural Science Foundation of Liaoning Province (Grant no. 20170520164), and the China Postdoctoral Science Foundation (Grant no. 2018M631806).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, China

    Yuliang Ma, Ye Yuan, Yishu Wang, Delong Ma & Pengjie Cui

  2. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

    Guoren Wang

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  1. Yuliang Ma
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  2. Ye Yuan
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  3. Guoren Wang
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  4. Yishu Wang
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  5. Delong Ma
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  6. Pengjie Cui
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Corresponding author

Correspondence to Yuliang Ma .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Guoliang Li

  2. Duke University, Durham, NC, USA

    Jun Yang

  3. University of Porto, Porto, Portugal

    Joao Gama

  4. Chiang Mai University, Chiang Mai, Thailand

    Juggapong Natwichai

  5. Beihang University, Beijing, China

    Yongxin Tong

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Ma, Y., Yuan, Y., Wang, G., Wang, Y., Ma, D., Cui, P. (2019). Local Experts Finding Across Multiple Social Networks. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11447. Springer, Cham. https://doi.org/10.1007/978-3-030-18579-4_32

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  • DOI: https://doi.org/10.1007/978-3-030-18579-4_32

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  • Online ISBN: 978-3-030-18579-4

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