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Efficient and Flexible Compression of Very Sparse Networks of Big Data

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Big Data and Social Media Analytics

Part of the book series: Lecture Notes in Social Networks ((LNSN))

Abstract

In the current era of big data, huge amounts of valuable data and information have been generated and collected at a very rapid rate from a wide variety of rich data sources. Social networks are examples of these rich data sources. Embedded in these big data are implicit, previously unknown and useful knowledge that can be mined and discovered by data science techniques such as data mining and social network analysis. Hence, these techniques have drawn attention of researchers. In general, a social network consists of many users (or social entities), who are often connected by “following” relationships. Finding those famous users who are frequently followed by a large number of common followers can be useful. These frequently followed groups of famous users can be of interest to many researchers (or businesses) due to their influential roles in the social networks. However, it can be challenging to find these frequently followed groups because most users are likely to follow only a small number of famous users. In this chapter, we present an efficient and flexible compression model for supporting the analysis and mining of very sparse networks of big data, from which the frequently followed groups of users can be discovered.

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Notes

  1. 1.

    https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/

  2. 2.

    https://www.linkedin.com/company/linkedin/about/

  3. 3.

    https://www.statista.com/statistics/234038/telegram-messenger-mau-users/

  4. 4.

    https://socialblade.com/facebook/top/50/likes

References

  1. Xylogiannopoulos, K. F., Karampelas, P., & Alhajj, R. (2019). Multivariate motif detection in local weather big data. In IEEE/ACM ASONAM 2019 (pp. 749–756). ACM.

    Google Scholar 

  2. Han, K., et al. (2019). Efficient and effective algorithms for clustering uncertain graphs. Proceedings of the VLDB Endowment, 12(6), 667–680.

    Article  Google Scholar 

  3. Ke, X., Khan, A., & Quan, L. L. H. (2019). An in-depth comparison of s-t reliability algorithms over uncertain graphs. Proceedings of the VLDB Endowment, 12(8), 864–876.

    Article  Google Scholar 

  4. Leung, C. K. (2014). Uncertain frequent pattern mining. In Frequent pattern mining (pp. 417–453).

    MATH  Google Scholar 

  5. Leung, C. K., Mateo, M. A. F., & Brajczuk, D. A. (2008). A tree-based approach for frequent pattern mining from uncertain data. In PAKDD 2008. LNCS (LNAI) (Vol. 5012, pp. 653–661).

    Google Scholar 

  6. Leung, C. K., & Carmichael, C. L. (2009). FpVAT: A visual analytic tool for supporting frequent pattern mining. ACM SIGKDD Explorations, 11(2), 39–48.

    Article  Google Scholar 

  7. Leung, C. K., et al. (2020). Big data visualization and visual analytics of COVID-19 data. In IV 2020 (pp. 387–392). https://doi.org/10.1109/IV51561.2020.00073.

    Chapter  Google Scholar 

  8. O’Halloran, S., et al. (2017). Computational data sciences and the regulation of banking and financial services. In From social data mining and analysis to prediction and community detection (pp. 179–209).

    Chapter  Google Scholar 

  9. Gupta, P., et al. (2020). Vertical data mining from relational data and its application to COVID-19 data. In Big data analyses, services, and smart data (pp. 106–116). https://doi.org/10.1007/978-981-15-8731-3_8.

    Chapter  Google Scholar 

  10. Leung, C. K., et al. (2020). Data science for healthcare predictive analytics. In IDEAS 2020 (pp. 8:1–8:10). ACM.

    Google Scholar 

  11. Olawoyin, A. M., Leung, C. K., & Choudhury, R. (2020). Privacy-preserving spatio-temporal patient data publishing. In DEXA 2020, Part II. LNCS (Vol. 12392, pp. 407–416).

    Google Scholar 

  12. Pawliszak, T., et al. (2020). Operon-based approach for the inference of rRNA and tRNA evolutionary histories in bacteria. BMC Genomics 21, (Supplement 2), 252:1–252:14.

    Google Scholar 

  13. Souza, J., Leung, C. K., & Cuzzocrea, A. (2020). An innovative big data predictive analytics framework over hybrid big data sources with an application for disease analytics. In AINA 2020. AISC (Vol. 1151, pp. 669–680).

    Google Scholar 

  14. Vural, H., Kaya, M., & Alhajj, R. (2019). A model based on random walk with restart to predict circRNA-disease associations on heterogeneous network. In IEEE/ACM ASONAM 2019 (pp. 929–932). ACM.

    Google Scholar 

  15. Hoang, K., et al. (2020). Cognitive and predictive analytics on big open data. In ICCC 2020. LNCS (Vol. 12408, pp. 88–104).

    Google Scholar 

  16. Leung, C. K., et al. (2020). Data mining on open public transit data for transportation analytics during pre-COVID-19 era and COVID-19 era. In INCoS 2020. AISC (Vol. 1263, pp. 133–144).

    Google Scholar 

  17. Fan, C., et al. (2018). Social network mining for recommendation of friends based on music interests. In IEEE/ACM ASONAM 2018 (pp. 833–840). IEEE.

    Google Scholar 

  18. Fariha, A., et al. (2013). Mining frequent patterns from human interactions in meetings using directed acyclic graphs. In PAKDD 2013, Part I. LNCS (LNAI) (Vol. 7818, pp. 38–49).

    Google Scholar 

  19. Ghaffar, F., et al. (2018). A framework for enterprise social network assessment and weak ties recommendation. In IEEE/ACM ASONAM 2018 (pp. 678–685). IEEE.

    Google Scholar 

  20. Jiang, F., Leung, C. K., & Tanbeer, S. K. (2012). Finding popular friends in social networks. In CGC 2012 (pp. 501–508). IEEE.

    Google Scholar 

  21. Leung, et al. (2018). Mining ’following’ patterns from big but sparsely distributed social network data. In IEEE/ACM ASONAM 2018 (pp. 916–919). IEEE.

    Google Scholar 

  22. Leung, C. K., Tanbeer, S. K., & Cameron, J. J. (2014). Interactive discovery of influential friends from social networks. Social Network Analysis and Mining, 4(1), 154:1–154:13.

    Article  Google Scholar 

  23. Patel, H., Paraskevopoulos, P., & Renz, M. (2018). GeoTeGra: A system for the creation of knowledge graph based on social network data with geographical and temporal information. In IEEE/ACM ASONAM 2018 (pp. 617–620). IEEE.

    Google Scholar 

  24. Rafailidis, D., & Crestani, F. (2018). Friend recommendation in location-based social networks via deep pairwise learning. In IEEE/ACM ASONAM 2018 (pp. 421–4428). IEEE.

    Google Scholar 

  25. Tanbeer, S. K., Leung, C. K., & Cameron, J. J. (2014). Interactive mining of strong friends from social networks and its applications in e-commerce. Journal of Organizational Computing and Electronic Commerce, 24(2–3), 157–173.

    Article  Google Scholar 

  26. Vaanunu, M., & Avin, C. (2018). Homophily and nationality assortativity among the most cited researchers’ social network. In IEEE/ACM ASONAM 2018 (pp. 584–586). IEEE.

    Google Scholar 

  27. Leung, C. K., et al. (2018). Big data analytics of social network data: Who cares most about you on Facebook? In Highlighting the importance of big data management and analysis for various applications (pp. 1–15). https://doi.org/10.1007/978-3-319-60255-4_1.

    Chapter  Google Scholar 

  28. Mai, M., et al. (2020). Big data analytics of Twitter data and its application for physician assistants: Who is talking about your profession in twitter? In Data management and analysis (pp. 17–32). https://doi.org/10.1007/978-3-030-32587-9_2.

    Chapter  Google Scholar 

  29. O’Halloran, S., et al. (2019). A data science approach to predict the impact of collateralization on systemic risk. In From security to community detection in social networking platforms (pp. 171–192).

    Chapter  Google Scholar 

  30. Leung, C. K. (2020). Data science for big data applications and services: Data lake management, data analytics and visualization. In Big data analyses, services, and smart data (pp. 28–44). https://doi.org/10.1007/978-981-15-8731-3_3.

    Chapter  Google Scholar 

  31. Das, A., et al. (2019). Water governance network analysis using Graphlet mining. In IEEE/ACM ASONAM 2019 (pp. 633–640). ACM.

    Google Scholar 

  32. Leung, C. K. (2020). Big data computing and mining in a smart world. In Big data analyses, services, and smart data (pp. 15–27). https://doi.org/10.1007/978-981-15-8731-3_2.

    Chapter  Google Scholar 

  33. Leung, C. K. (2018). Frequent Itemset mining with constraints. In Encyclopedia of database systems (2nd ed., pp. 1531–1536).

    Chapter  Google Scholar 

  34. Arora, U., Paka, W. S., & Chakraborty, T. (2019). Multitask learning for blackmarket tweet detection. In IEEE/ACM ASONAM 2019 (pp. 127–130). ACM.

    Google Scholar 

  35. Leung, C. K., MacKinnon, R. K., & Wang, Y. (2014). A machine learning approach for stock price prediction. In IDEAS 2014 (pp. 274–277). ACM.

    Google Scholar 

  36. Leung, C. K., Jiang, F., & Zhang, Y. (2019). Flexible compression of big data. In IEEE/ACM ASONAM 2019 (pp. 741–748). ACM.

    Google Scholar 

  37. Cao, Y., et al. (2020). Hybrid deep learning model assisted data compression and classification for efficient data delivery in mobile health applications. IEEE Access, 8, 94757–94766.

    Article  Google Scholar 

  38. Jiang, H., & Lin, S. (2020). A rolling hash algorithm and the implementation to LZ4 data compression. IEEE Access, 8, 35529–35534.

    Article  Google Scholar 

  39. Birman, R., Segal, Y., & Hadar, O. (2020). Overview of research in the field of video compression using deep neural networks. Multimedia Tools and Applications, 79(17–18), 11699–11722.

    Article  Google Scholar 

  40. Fu, H., Liang, F., & Lei, B. (2020). An extended hybrid image compression based on soft-to-hard quantification. IEEE Access, 8, 95832–95842.

    Article  Google Scholar 

  41. Kumar, K. S., Kumar, S. S., & Kumar, N. M. (2020). Efficient video compression and improving quality of video in communication for computer encoding applications. Computer Communications, 153, 152–158.

    Article  Google Scholar 

  42. Liu, T., & Wu, Y. (2020). Multimedia image compression method based on biorthogonal wavelet and edge intelligent analysis. IEEE Access, 8, 67354–67365.

    Article  Google Scholar 

  43. Hossein, S. M., et al. (2020). DNA sequences compression by GP2 R and selective encryption using modified RSA technique. IEEE Access, 8, 76880–76895.

    Article  Google Scholar 

  44. Kounelis, F., & Makris, C. (2020). Comparison between text compression algorithms in biological sequences. Information and Computation, 270, 104466:1–104466:8.

    Article  MathSciNet  Google Scholar 

  45. Hernández, C., & Marín, M. (2013). Discovering dense subgraphs in parallel for compressing web and social networks. In SPIRE 2013. LNCS (Vol. 8214, pp. 165–173).

    Google Scholar 

  46. Liu, Z., Ma, Y., & Wang, X. (2020). A compression-based multi-objective evolutionary algorithm for community detection in social networks. IEEE Access, 8, 62137–62150.

    Article  Google Scholar 

  47. Leung, C. K., et al. (2016). Mining “following” patterns from big sparse social networks. In IEEE/ACM ASONAM 2016 (pp. 923–930). IEEE.

    Google Scholar 

  48. Leung, C. K., & Jiang, F. (2017). Efficient mining of “following” patterns from very big but sparse social networks. In IEEE/ACM ASONAM 2017 (pp. 1025–1032). ACM.

    Google Scholar 

  49. Leskovec, J., & Krevl, A. (2014). SNAP datasets: Stanford large network dataset collection. http://snap.stanford.edu/data.

  50. McAuley, J., & Leskovec, J. (2012). Learning to discover social circles in ego networks. In NIPS 2012 (pp. 548–556).

    Google Scholar 

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Acknowledgement

This work is partially supported by (1) Natural Sciences and Engineering Research Council (NSERC) of Canada, and (2) University of Manitoba.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada

    Carson K. Leung & Yibin Zhang

  2. Department of Computer Science, University of Northern British Columbia (UNBC), Prince George, BC, Canada

    Fan Jiang

  3. Department of Computer Science, University of Toronto, Toronto, ON, Canada

    Yibin Zhang

Authors
  1. Carson K. Leung
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  2. Fan Jiang
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  3. Yibin Zhang
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Corresponding author

Correspondence to Carson K. Leung .

Editor information

Editors and Affiliations

  1. Bilkent yerleşkesi, Turkish Ministry of Health, Çankaya, Ankara, Turkey

    Mehmet Çakırtaş

  2. Computer Engineering, Istanbul Medipol University, Istanbul, Turkey

    Mehmet Kemal Ozdemir

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Leung, C.K., Jiang, F., Zhang, Y. (2021). Efficient and Flexible Compression of Very Sparse Networks of Big Data. In: Çakırtaş, M., Ozdemir, M.K. (eds) Big Data and Social Media Analytics. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-67044-3_9

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