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International Conference on Big Data Analytics and Knowledge Discovery

DaWaK 2015: Big Data Analytics and Knowledge Discovery pp 123–135Cite as

Big Data Analytics of Social Networks for the Discovery of “Following” Patterns

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9263))

Abstract

In the current era of big data, high volumes of valuable data can be easily collected and generated. Social networks are examples of generating sources of these big data. Users (or social entities) in these social networks are often linked by some interdependency such as friendship or “following” relationships. As these big social networks keep growing, there are situations in which individual users or businesses want to find those frequently followed groups of social entities so that they can follow the same groups. In this paper, we present a big data analytics solution that uses the MapReduce model to mine social networks for discovering groups of frequently followed social entities. Evaluation results show the efficiency and practicality of our big data analytics solution in discovering “following” patterns from social networks.

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References

  1. Cuzzocrea, A., Leung, C.K.-S., MacKinnon, R.K.: Mining constrained frequent itemsets from distributed uncertain data. Future Gener. Comput. Syst. 37, 117–126 (2014)

    Article  Google Scholar 

  2. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  3. Dhahri, N., Trabelsi, C., Ben Yahia, S.: RssE-Miner: a new approach for efficient events mining from social media RSS feeds. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2012. LNCS, vol. 7448, pp. 253–264. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  4. Jiang, F., Leung, C.K.-S.: Mining interesting “Following” patterns from social networks. In: Bellatreche, L., Mohania, M.K. (eds.) DaWaK 2014. LNCS, vol. 8646, pp. 308–319. Springer, Heidelberg (2014)

    Google Scholar 

  5. Jiang, F., Leung, C.K.-S.: Stream mining of frequent patterns from delayed batches of uncertain data. In: Bellatreche, L., Mohania, M.K. (eds.) DaWaK 2013. LNCS, vol. 8057, pp. 209–221. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  6. Jiang, F., Leung, C.K.-S., Liu, D., Peddle, A.M.: Discovery of really popular friends from social networks. In: IEEE BDCloud 2014, pp. 342–349. IEEE, Los Alamitos (2014)

    Google Scholar 

  7. Kang, Y., Yu, B., Wang, W., Meng, D.: Spectral Clustering for Large-Scale Social Networks via a Pre-Coarsening Sampling based Nyström Method. In: Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015, Part II. LNCS (LNAI), vol. 9078, pp. 106–118. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

  8. Leung, C.K.-S., Cuzzocrea, A., Jiang, F.: Discovering frequent patterns from uncertain data streams with time-fading and landmark models. LNCS TLDKS 8, 174–196 (2013)

    Google Scholar 

  9. Leung, C.K.-S., MacKinnon, R.K.: BLIMP: a compact tree structure for uncertain frequent pattern mining. In: Bellatreche, L., Mohania, M.K. (eds.) DaWaK 2014. LNCS, vol. 8646, pp. 115–123. Springer, Heidelberg (2014)

    Google Scholar 

  10. Leung, C.K.-S., MacKinnon, R.K., Tanbeer, S.K.: Fast algorithms for frequent itemset mining from uncertain data. In: Kumar, R., Toivonen, H., Pei, J., Huang, J.Z., Wu, X. (eds.) IEEE ICDM 2014, pp. 893–898. IEEE, Los Alamitos (2014)

    Google Scholar 

  11. Leung, C.K.-S., Tanbeer, S.K.: Mining popular patterns from transactional databases. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2012. LNCS, vol. 7448, pp. 291–302. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  12. Leung, C.K.-S., Tanbeer, S.K., Cameron, J.J.: Interactive discovery of influential friends from social networks. Soc. Netw. Anal. Min. 4(1), Article 154 (2014)

    Google Scholar 

  13. Ma, L., Huang, H., He, Q., Chiew, K., Wu, J., Che, Y.: GMAC: a seed-insensitive approach to local community detection. In: Bellatreche, L., Mohania, M.K. (eds.) DaWaK 2013. LNCS, vol. 8057, pp. 297–308. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  14. Madden, S.: From databases to big data. IEEE Internet Comput. 16(3), 4–6 (2012)

    Article  Google Scholar 

  15. Mumu, T.S., Ezeife, C.I.: Discovering community preference influence network by social network opinion posts Mining. In: Bellatreche, L., Mohania, M.K. (eds.) DaWaK 2014. LNCS, vol. 8646, pp. 136–145. Springer, Heidelberg (2014)

    Google Scholar 

  16. Rader, E., Gray, R.: Understanding user beliefs about algorithmic curation in the facebook news feed. In: Begole, B., Kim, J., Inkpen, K., Woo, W. (eds.) ACM CHI 2015, pp. 173–182. ACM, New York (2015)

    Google Scholar 

  17. Rajadesingan, A., Zafarani, R., Liu, H.: Sarcasm detection on Twitter: a behavioral modeling approach. In: Cheng, X., Li, H., Gabrilovich, E., Tang, J. (eds.) ACM WSDM 2015, pp. 97–106. ACM, New York (2015)

    Google Scholar 

  18. Tanbeer, S.K., Leung, C.K.-S., Cameron, J.J.: Interactive mining of strong friends from social networks and its applications in e-commerce. J. Organ. Comput. Electron. Commer. 24(2–3), 157–173 (2014)

    Google Scholar 

  19. Wei, E.H.-C., Koh, Y.S., Dobbie, G.: Finding maximal overlapping communities. In: Bellatreche, L., Mohania, M.K. (eds.) DaWaK 2013. LNCS, vol. 8057, pp. 309–316. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  20. Yu, W., Coenen, F., Zito, M., El Salhi, S.: Minimal vertex unique labelled subgraph mining. In: Bellatreche, L., Mohania, M.K. (eds.) DaWaK 2013. LNCS, vol. 8057, pp. 317–326. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

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Acknowledgement

This project is partially supported by NSERC (Canada) and University of Manitoba.

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Authors and Affiliations

  1. University of Manitoba, Winnipeg, MB, Canada

    Carson Kai-Sang Leung & Fan Jiang

Authors
  1. Carson Kai-Sang Leung
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  2. Fan Jiang
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Corresponding author

Correspondence to Carson Kai-Sang Leung .

Editor information

Editors and Affiliations

  1. University of Science and Technology, Rolla, Missouri, USA

    Sanjay Madria

  2. Osaka University, Osaka, Japan

    Takahiro Hara

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© 2015 Springer International Publishing Switzerland

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Leung, C.KS., Jiang, F. (2015). Big Data Analytics of Social Networks for the Discovery of “Following” Patterns. In: Madria, S., Hara, T. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2015. Lecture Notes in Computer Science(), vol 9263. Springer, Cham. https://doi.org/10.1007/978-3-319-22729-0_10

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  • DOI: https://doi.org/10.1007/978-3-319-22729-0_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22728-3

  • Online ISBN: 978-3-319-22729-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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