Graph Analysis and Visualization of Social Network Big Data

  • N. Mithili Devi
  • Sandhya Rani Kasireddy
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)


In this fast growing Big Data oriented business world, all most every company is trying to identify new ways to capture and utilize unlimited stream of unstructured heterogeneous data efficiently. In this process companies are finding that Graph based representation of data is more beneficial and comfortable for their analysis methodologies. Development of Graph based tools are helpful for studying, transforming, visualizing and analyzing Big Data in the form of vertices and edges. Graphs are extremely useful to visualize hidden relationships among unstructured complex data sets. The popularity of Graphs has shown a stable growth with the evolution of the internet and social networks. Even though Graphs offer a flexible data structure, handling of Large-scale Graphs is an interesting research problem. Graph analysis and visualization are in the spotlight because of its ability to adapt it for social networking analysis systems. Sales and marketing managers are making use of Analysis and Visualization of Social networking Graph based system to meet their business targets and sustain at top position in the market. Successful implementation of Graph analytics revolves around quite a lot of key considerations such as collect the data, clean it, build the Graph, compresses, filters, transform, visualize and Analyze it. This paper concentrates on creating, transforming, visualizing and analyzing Large-scale Graphs from sample data pertaining to product purchase from Amazon social networking website.


Big data Social networks Large-scale graphs Graph analysis and visualization 


  1. 1.
    Khan N, Yaqoob I, Hashem IAT et al (2014) Big data: survey, technologies, opportunities, and challenges. Sci. World J. 2014:712826. Scholar
  2. 2.
    García S, Ramírez-Gallego S et al (2016) Big data preprocessing: methods and prospects. Big Data Anal 1(1):9Google Scholar
  3. 3.
    Trujillo G et al (2014) Understanding the big data world. Pearson IT Certification. Retrieved 26 Nov 2017 from Accessed on 20 Aug 2014
  4. 4.
    Gill NS (2017) Data ingestion, processing and architecture layers for Big data and IoT. Retrieved 12 Dec 2017 from Accessed on 03 Mar 2017
  5. 5.
    Geetha K, VijayaKathiravan A (2014) A parallelized social net-work analysis using virtualization 320 for student’s academic improvement. IJIRCCE 2(5): 136–144Google Scholar
  6. 6.
    Octparse (2017) Top 30 big data tools for data analysis. Retrieved 7 Oct 2017 from Accessed on 16 Aug 2017
  7. 7.
    Cohen S (2016) Data management for social networking. In: Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI symposium on principles of database systems, pp 165–177Google Scholar
  8. 8.
    Du X, Ye Y, Li Y, Li Y (2017) SGP: sampling big social network based on graph partition. IEEE Xplore.
  9. 9.
    Camberlain BP et al (2018) Real-time community detection in full social networks on a laptop. Scholar
  10. 10.
    Aridhi S, Montresor A, Velegraki Y (2017) BLADYG: a graph processing framework for large dynamic graphs. J Big Data Res 9:9–17CrossRefGoogle Scholar
  11. 11.
  12. 12.
    Canadian Business Network Importance of knowledge to a growing business. Retrieved 23 Dec 2017 from
  13. 13.
    Wu Q, Qi X, Fuller E, Zhang C-Q (2013) “Follow the Leader”: a centrality guided clustering and its application to social network analysis. Sci World J 2013:368568. Scholar
  14. 14.
    Joseph J et al (2011) Methods to determine node centrality and clustering in graphs with uncertain  structure,
  15. 15.
    Jonker D, Brath R (2015)Graph analysis and visualization: discovering business opportunity in  linked data. ISBN 1118845844, Wiley PublicationGoogle Scholar
  16. 16.
    Akthar N et al (2014) Social network analysis tools, Scholar
  17. 17.
    Akhtar N, Javed H, Sengar G (2013) Analysis of facebook social network. In: IEEE international conference on computational intelligence and computer networks (CICN), Mathura, India, 27–29 Sept 2013Google Scholar
  18. 18.
    Connected components. Retrieved 23 Feb 2018 from
  19. 19.
  20. 20.
    Strang A, Haynes O et al(2017), Relationships between characteristic path length, efficiency, clustering coefficients, and graph density,

Copyright information

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceSri Padmavati Mahila VisvavidyalayamTirupatiIndia
  2. 2.Dean SciencesSPMVVTirupatiIndia

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