Computational Social Networks: Tools, Perspectives, and Challenges

  • Mrutyunjaya Panda
  • Nashwa El-Bendary
  • Mostafa A. Salama
  • Aboul Ella Hassanien
  • Ajith Abraham


Computational social science is a new emerging field that has overlapping regions from mathematics, psychology, computer sciences, sociology, and management. Social computing is concerned with the intersection of social behavior and computational systems. It supports any sort of social behavior in or through computational systems. It is based on creating or recreating social conventions and social contexts through the use of software and technology. Thus, blogs, email, instant messaging, social network services, wikis, social bookmarking, and other instances of what is often called social software illustrate ideas from social computing. Social network analysis is the study of relationships among social entities. It is becoming an important tool for investigators. However all the necessary information is often distributed over a number of websites. Interest in this field is blossoming as traditional practitioners in the social and behavioral sciences are being joined by researchers from statistics, graph theory, machine learning, and data mining. In this chapter, we illustrate the concept of social networks from a computational point of view, with a focus on practical services, tools, and applications and open avenues for further research. Challenges to be addressed and future directions of research are presented and an extensive bibliography is also included.


Social Network Analysis Social Network Site Analytic Network Process Short Path Problem Social Network Service 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Snásel, V., Horak, Z., Abraham, A.: Understanding social networks using formal concept analysis. In: Web Intelligence/IAT Workshops’2008, Sydney, pp. 390–393 (2008)Google Scholar
  2. 2.
    Alexander, B.: Web 2.0: a new wave of innovation for teaching and learning? EDUCAUSE Rev. 41(2), 32–44 (2006)Google Scholar
  3. 3.
    Hoffman, E.: Evaluating social networking tools for distance learning. In: Proceedings of Technology, Colleges and Community Worldwide Online Conference (TCC 2009) Volume 2009, Hawaii, vol. 1, pp. 92–100 (2009)Google Scholar
  4. 4.
    Brian Solis: The Essential Guide to Social Media, e-book (2008)Google Scholar
  5. 5.
    Boyd, D., Ellison, N.: Social network sites: definition, history and scholarship. J. Comput. Mediat. Commun. 13(1), 210–230 (2007)CrossRefGoogle Scholar
  6. 6.
    Thellwal, M.: Social networks, gender and friending, analysis of Myspace profiles. J. Am. Soc. Inf. Sci. Technol. 591(8), 1321–1330 (2008)CrossRefGoogle Scholar
  7. 7.
    Tufekci, Z.: Grooming, gossip Facebook and Myspace: what can we learn about these sites from those who wont assimilate? J. Inf. Commun. Soc. 11(4), 544–564 (2008)CrossRefGoogle Scholar
  8. 8.
    Sheldon, P.: The relationship between unwillingness to communicate and students’ Facebook use. J. Media Psychol. 20(2), 67–75 (2008)Google Scholar
  9. 9.
    Schrock, A.: Eamining social media usage: technology clusters and social network relationships. 14(1) (2009)Google Scholar
  10. 10.
    Walther, J., Heide, B., Kim, S., Westerman, D., Tang, S.T.: The role of friends appearence and behaviour on evaluations of individuals on facebook: are we known by the comapny we keep? Hum. Commun. Res. 34, 28–49 (2008)CrossRefGoogle Scholar
  11. 11.
    Moor, J.H.: Towards a theory of privacy in the information age. SIGCAS Comput. Soc. 27(3), 27–32 (1997)CrossRefGoogle Scholar
  12. 12.
    Grace, J., Gruhl, D., Haas, K., Nagarajan, M., Robson, C., Sahoo, N.: Artist ranking through analysis of online community comments. In: IBM Tech Report, Almaden (2008)Google Scholar
  13. 13.
    Shani, G., Chickering, M., Meek, C.: Mining recommendations from the web. In: Proceeding of 2008 ACM Conference on Recommender System, Lausanne, pp. 35–42 (2008)Google Scholar
  14. 14.
    Stutzman, F.: Thoughts about information, social networks, identity and technology. Social Network Transitions, Unit Structures (2007)Google Scholar
  15. 15.
  16. 16.
    Shivalingaiah, D., Naik, U.: Social networking tools: social bookmarking and social tagging. In: Proceedings of the 8th International CALIBER-2011, Goa University, Goa, 02–04 March 2011Google Scholar
  17. 17.
    Brett: 10 Open source social bookmarking platforms. Available at Accessed Nov 2011
  18. 18.
    Golbeck, J., Halaschek-Wiener, C.: Trust-based revision for expressive web syndication. J. Logic Comput. 19(5), 771–790 (2008)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Sigurbjörnsson, B., Zwol, R.: Flickr tag recommendation based on collective knowledge. In: Proceeding of the 17th International Conference on World Wide Web, ACM, New York, pp. 327–336 (2008)Google Scholar
  20. 20.
    Chen, S.: The social network game boom, Gamasutra (2009)Google Scholar
  21. 21.
    Radoff, J.: History of social games. Available at Accessed Sept 2010
  22. 22.
    Grossman, L.: The odd popularity of Mafia Wars. TIME (2009)Google Scholar
  23. 23.
    Järvinen, A.: Game design for social networks. In: Proceedings of the 13th International MindTrek Conference: Everyday Life in the Ubiquitous Era, Tampere (2009)Google Scholar
  24. 24.
    Kim, R.: The future of social games is mobile. GigaOM. Available at Accessed Nov 2011
  25. 25.
    Kleinman, Z.: Social network games catch the eye of computer giants. J. Comput.-Mediat. Commun. 12(4), 1143–1168 (2009)MathSciNetGoogle Scholar
  26. 26.
    Kohler, C.: 14. Happy Farm (2008). The 15 most influential games of the decade, Wired (2011)Google Scholar
  27. 27.
    YALSA: Teens & Social Networking in the School & Public Library, American Library Association: Young Adult Library Services Association (YALSA) (2007)Google Scholar
  28. 28.
    Godwin, S., Thorpe, M., Richardson, J.: The impact of computer-mediated interaction on distance learning. Br. J. Educ. Technol. 39(1), 52–70 (2008)Google Scholar
  29. 29.
    Mazer, J.P., Murphy, R.E., Simonds, C.J.: I’ll see you on “Facebook”: the effects of computer-mediated teacher self-disclosure on student motivation, affective learning, and classroom climate. Commun. Edu. 56(1), 1–17 (2007)CrossRefGoogle Scholar
  30. 30.
    Saint-Charles, J., Mongeau, P.: Different relationships for coping with ambiguity and uncertainty in organizations. Soc. Netw. 31, 33–39 (2009)CrossRefGoogle Scholar
  31. 31.
    Antheunis, M.L., Valkenburg, P.M., Peter, J.: Getting acquainted through social network sites: testing a model of online uncertainty reduction and social attraction. Comput. Hum. Behav. 26, 100–109 (2010)CrossRefGoogle Scholar
  32. 32.
    Gutiérrez-Muñoz, A., Kandel, A.: Current flows in electrical networks for Fuzzy social network analysis (FSNA). Master’s thesis, Department of Computer Science and Engineering, University of South Florida (2009)Google Scholar
  33. 33.
    Yang, W.Z., Ge, Y.H., He, J.J., Liu, B.: Designing a group decision support system under uncertainty using group Fuzzy analytic network process (ANP). Afr. J. Bus. Manage. 4(12), 2571–2585 (2010)Google Scholar
  34. 34.
    Hassan, S., Garmendia, L., Pavon, J.: Introducing uncertainty into social simulation: using Fuzzy logic for agent-based modelling. Int. J. Reason.-based Intell. Syst. 2(2), 118–124 (2010)Google Scholar
  35. 35.
    Vindigni, G., Janssen, M.A., Jager, W.: Organic food consumption: a multi-theoretical framework of consumer decision making. Br. Food J. 104(8), 624–642 (2002)CrossRefGoogle Scholar
  36. 36.
    Moustafa, W., Deshpande, A., Namata, G., Getoor, L.: Declarative analysis of noisy information networks. In: Proceedings of IEEE 27th International Conference on Department of Computer Science, Data Engineering Workshops (ICDEW’11), IEEE Computer Society, pp. 106–111 (2011)Google Scholar
  37. 37.
    Casey, E.: Error, uncertainty, and loss in digital evidence. Int. J. Digit. Evid. 1(2) (2002)Google Scholar
  38. 38.
    Sommer, C.: Approximate shortest path and distance queries in network. PhD Thesis, Department of Computer Science Graduate School of Information Science and Technology, The University of Tokyo (2010)Google Scholar
  39. 39.
    Michlmayr, E.: Ant algorithms for self-organization in social networks. PhD thesis, Vienna University of Technology, Faculty of Informatics (2007)Google Scholar
  40. 40.
    Lertsuwanakul, L., Unger, H.: An improved greedy routing algorithm for grid using pheromone-based landmark. World Academy of Science, Engineering and Technology (2009)Google Scholar
  41. 41.
    Perumbuduru, S., Dhar, J.: Performance evaluation of different network topologies based on Ant colony optimization. Int. J. Wirel. Mob. Netw. 2(4), 141–157 (2010)CrossRefGoogle Scholar
  42. 42.
    Kumar, R., Kumar, M.: Genetic algorithm for shortest path optimization in data networks. Glob. J. Comput. Sci. Technol. (2010)Google Scholar
  43. 43.
    Cauvery, N., Viswanatha, K.V.: Routing in dynamic network using ants genetic algorithm. IJCSNS 9(3) (2009)Google Scholar
  44. 44.
    White, T., Pagurek, B., Oppacher, F.: ASGA: improving the ant system by integration with genetic algorithms. In: Proceedings of the Third Annual Conference, University of Wisconsin, pp. 610–617 (1999)Google Scholar
  45. 45.
    Araujo, F., Ribeiro, B., Rodrigues, L.: A neural network for Shortest path computation. IEEE Trans. Neural Netw. 12(5), 1067–1073 (2001)CrossRefGoogle Scholar
  46. 46.
    Sang, Y., YI, Z.: A modified pulse coupled neural network for shortest path computation. J. Comput. Inf. Syst. 6(9), 3095–3102 (2010)Google Scholar
  47. 47.
    Deng, Y., Tong, H.: Dynamic shortest path algorithm in stochastic traffic networks using PSO based on fluid neural network. J. Intell. Learn. Syst. Appl. 3, 11–16 (2011)Google Scholar
  48. 48.
    White, C., Plotnick, L., Kushma, J., Hiltz, S.R., Turoff, M.: An online social network for emergency management. Int. J. Emerg. Manage. 6(3/4) 2009Google Scholar
  49. 49.
    Hobson, J., Cook, S.: Social media for researchers: opportunities and challenges. MAI (2011)Google Scholar
  50. 50.

Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  • Mrutyunjaya Panda
    • 1
  • Nashwa El-Bendary
    • 2
  • Mostafa A. Salama
    • 3
  • Aboul Ella Hassanien
    • 4
  • Ajith Abraham
    • 5
  1. 1.Department of ECEGandhi Institute for Technological Advancement (GITA)OdishaIndia
  2. 2.Arab Academy for Science, Technology, and Maritime TransportCairoEgypt
  3. 3.Department of Computer ScienceBritish University in EgyptCairoEgypt
  4. 4.Faculty of Computers and InformationCairo UniversityCairoEgypt
  5. 5.Machine Intelligence Research Labs (MIR Labs)Scientific Network for Innovation and Research ExcellenceAuburnUSA

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