Advertisement

Wireless Personal Communications

, Volume 97, Issue 3, pp 3519–3529 | Cite as

Piecewise Maximal Similarity for Ad-hoc Social Networks

  • Sapna Gambhir
  • Nagender AnejaEmail author
  • Liyanage Chandratilake De Silva
Article

Abstract

Computing Profile Similarity is a fundamental requirement in the area of Social Networks to suggest similar social connections that have high chance of being accepted as actual connection. Representing and measuring similarity appropriately is a pursuit of many researchers. Cosine similarity is a widely used metric that is simple and effective. This paper provides analysis of cosine similarity for social profiles and proposes a novel method to compute Piecewise Maximal Similarity between profiles. The proposed metric is 6% more effective to measure similarity than cosine similarity based on computations on real data.

Keywords

Ad-hoc Social Network Profile Similarity User profile Mobile Ad-hoc Social Network 

References

  1. 1.
    Anderson, A., Huttenlocher, D., Kleinberg, J., & Leskovec, J. (2012). Effects of user similarity in social media. doi: 10.1145/2124295.2124378.
  2. 2.
    Aneja, N., & Gambhir, S. (2012). Various issues in ad-hoc social networks. In: Proceedings of National Conference on Recent Trends in Computer Scince and Information Technology (RTCSIT) (pp. 6–9). Echelon Institute of Technology, Faridabad, Haryana, India. http://echeloninstitute.org/images/download/proceedingRTCSIT.pdf.
  3. 3.
    Aneja, N., & Gambhir, S. (2014). Geo-social profile matching algorithm for dynamic interests in ad-hoc social network. Social Networking, 3(5), 240–247. doi: 10.4236/sn.2014.35029. http://www.scirp.org/journal/PaperDownload.aspx?paperID=51108.
  4. 4.
    Aneja, N., & Gambhir, S. (2015). Geo-social semantic profile matching algorithm for dynamic interests in ad-hoc social network. In: 2015 IEEE International Conference on Computational Intelligence and Communication Technology (CICT). IEEE.Google Scholar
  5. 5.
    Caputo, A., Socievole, A., & Rango, F. D. (2015). CRAWDAD dataset unical/socialblueconn (v. 2015-02-08). Downloaded from http://crawdad.org/unical/socialblueconn/20150208 doi: 10.15783/C7GK5R.
  6. 6.
    Chigozie, O., Williams, P., & Osegi, N. E. (2015). Hybrid social networking application for a university community. CoRR abs/1503.05642. http://arxiv.org/abs/1503.05642.
  7. 7.
    Chung, E., Joy, J., & Gerla, M. (2015). Discoverfriends: Secure social network communication in mobile ad hoc networks. CoRR abs/1505.07487. http://arxiv.org/abs/1505.07487.
  8. 8.
    Facebook. (2004). Facebook. https://www.facebook.com/.
  9. 9.
    Gambhir, S., & Aneja, N. (2013). Ad-hoc social network: A comprehensive survey. International Journal of Scientific and Engineering Research, 4(8), 156–160. http://www.ijser.org/researchpaper%5CAd-hoc-Social-Network-A-Comprehensive-Survey.pdf.
  10. 10.
    Gambhir, S., Aneja, N., & Mangla, S. (2015). Need of ad-hoc social network based on users dynamic interests. In: IEEE 2015 International Conference on Soft Computing Techniques and Implementation (ICSCTI). IEEE.Google Scholar
  11. 11.
    Google. (2011). Google+. https://plus.google.com/.
  12. 12.
    Han, X., Wang, L., Crespi, N., Park, S., & Cuevas, A. (2015). Alike people, alike interests? Inferring interest similarity in online social networks. Decision Support Systems, 69(C), 92–106. doi: 10.1016/j.dss.2014.11.008.CrossRefGoogle Scholar
  13. 13.
    Kraus, N., Carmel, D., Keidar, I., & Orenbach, M. (2015). NearBucket-LSH: Efficient similarity search in P2P networks. http://arxiv.org/abs/1511.07148.
  14. 14.
    Lee, J., & Hong, C. S. (2011). A mechanism for building ad-hoc social network based on user’s interest. doi: 10.1109/APNOMS.2011.6076997.
  15. 15.
    LinkedIn. (2002). LinkedIn. https://www.linkedin.com/.
  16. 16.
    Mangla, S., & Gambhir, S. (2015). Research challenges in ad-hoc social network (ASN). International Journal of Advance Research in Science and Engineering, 4(1), 124–128. http://www.ijarse.com/images/fullpdf/159a.pdf.
  17. 17.
    Mizzaro, S., Pavan, M., & Scagnetto, I. (2015). Content based similarity of twitter users. Lecture Notes in Computer Science, Advances in Information Retrieval, Vol. 9022.Google Scholar
  18. 18.
    Socievole, A., De Rango, F., & Caputo, A. (2014). Wireless contacts, facebook friendships and interests: Analysis of a multi-layer social network in an academic environment. In: Wireless Days (WD), 2014 IFIP, pp. 1–7. doi: 10.1109/WD.2014.7020819.
  19. 19.
    Spertus, E., Sahami, M., & Buyukkokten, O. (2005). Evaluating similarity measures: A large-scale study in the orkut social network. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, KDD ’05 (pp. 678–684). ACM, New York, NY, USA. doi: 10.1145/1081870.1081956. http://doi.acm.org/10.1145/1081870.1081956.
  20. 20.
    Twitter. (2006). Twitter. https://twitter.com/.
  21. 21.
    Zhang, D., Zhang, D., Xiong, H., Hsu, C. H., & Vasilakos, A. (2014). Basa: Building mobile ad-hoc social networks on top of android. IEEE Network, 28(1), 4–9. doi: 10.1109/MNET.2014.6724100.CrossRefGoogle Scholar
  22. 22.
    Zhang, Y., Tang, J., Yang, Z., Pei, J., & Yu, P. S. (2015). Cosnet: Connecting heterogeneous social networks with local and global consistency. doi: 10.1145/2783258.2783268. http://doi.acm.org/10.1145/2783258.2783268.

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Computer EngineeringYMCA University of Science and TechnologyFaridabadIndia
  2. 2.Faculty of Integrated TechnologiesUniversiti Brunei DarussalamGadongBrunei Darussalam

Personalised recommendations