Prediction of Social Dimensions in a Heterogeneous Social Network

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 705)

Abstract

Advancements in communication and computing technologies allow people located geographically apart to meet on a common platform to share information with each other. Social networking sites play an important role in this aspect. A lot of information can be inferred from such networks if the data is analyzed appropriately by applying a relevant data mining method. The proposed work concentrates on leveraging the connection information of the nodes in a social network for the prediction of social dimensions of new nodes joining the social network. In this work, an edge clustering algorithm and a multilabel classification algorithm are proposed to predict the social dimensions of the nodes joining an existing social network. The results of the proposed algorithms are found out to be satisfactory.

References

  1. 1.
    Gundecha, P., Liu, H.: Mining Social Media: a brief introduction. Tutorials in Operations Research, Informs (2012)Google Scholar
  2. 2.
    Jensen, D., Neville, J., Gallagher, B.: Why collective inference improves relational classification. In: Proceeding of the 10th ACM SIGKKD International Conference on Knowledge Discovery and Data Mining, pp. 593–598. Seattle, WA, USA, August (2004)Google Scholar
  3. 3.
    Tang, L., Liu, H.: Leveraging social media networks for classification. Data Min. Knowl. Discov. 33(3), 447–478 (2011)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Tang, L., Liu, H.: Relational learning via latent social dimensions. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge discovery and Data Mining, pp. 817–826. New York, USA (2009)Google Scholar
  5. 5.
    Tang, L., Wang, X., Liu, H.: Scalable learning of collective behavior. IEEE Trans. Knowl. Data Eng. 24(4), 1080–1091 (2012)CrossRefGoogle Scholar
  6. 6.
    Tang, L., Liu, H.: Toward collective behavior prediction via social dimension extraction. IEEE Int. Syst. 25(4), 19–25 (2010)Google Scholar
  7. 7.
    BlogCatalog. (2013). http://www.blogcatalog.com
  8. 8.
    Zafarani, R., Liu, H.: Social Computing Data Repository at ASU, Tempe, AZ: Arizona State University, School of Computing, Informatics and Decision Systems EngineeringGoogle Scholar
  9. 9.

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Information and Communication Technology, Manipal Institute of TechnologyManipal Academy of Higher EducationManipal 576104India

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