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Mining of Influencers in Signed Social Networks: A Memetic Approach

  • Nancy Girdhar
  • K. K. Bharadwaj
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11278)

Abstract

The tenacious unfurl of social networks and its unfathomable influence into the daily lives of users is overwhelming that tempts researchers to explore and analyze the domain of social influence mining. To date, most of the research tends to focus only on positive influence for discovering influencers however, in signed social networks (SSNs) where besides positive links there are negative links that ascertain the presence of negative influence also. Thus, it is essential to consider both positive and negative influences to mine influential nodes in SSNs. In this work, we propose a novel approach based on memetic algorithm (MA) for finding set of influential users in a SSN. Our contribution is twofold. First, we formulate a new fitness function termed as Status Influential Strength (SIS) grounded on status theory and strength of links between users. Next, we propose a new approach for Mining Influencers based on Memetic Algorithm (MIMA) in signed social networks. The performance of proposed approach is validated through various experiments conducted on real-world Epinions dataset and the results clearly establish the efficacy of our proposed approach.

Keywords

Signed social networks Memetic algorithm Social influence mining Discovering influencers Status theory 

References

  1. 1.
    Agarwal, V., Bharadwaj, K.K.: Predicting the dynamics of social circles in ego networks using pattern analysis and GA K-means clustering. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 5(3), 113–141 (2015)CrossRefGoogle Scholar
  2. 2.
    Ahmed, S., Ezeife, C.I.: Discovering influential nodes from trust network. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing, pp. 121–128 (2013)Google Scholar
  3. 3.
    Awal, G.K., Bharadwaj, K.K.: Mining set of influencers in signed social networks with maximal collective influential power: a genetic algorithm approach. In: Satapathy, S.C., Joshi, A. (eds.) ICTIS 2017. SIST, vol. 84, pp. 263–274. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-63645-0_29CrossRefGoogle Scholar
  4. 4.
    Bharathi, S., Kempe, D., Salek, M.: Competitive influence maximization in social networks. In: Deng, X., Graham, F.C. (eds.) WINE 2007. LNCS, vol. 4858, pp. 306–311. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-77105-0_31CrossRefGoogle Scholar
  5. 5.
    Bonchi, F.: Influence propagation in social networks: a data mining perspective. IEEE Intell. Inform. Bull. 12(1), 8–16 (2011)Google Scholar
  6. 6.
    Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: Proceedings of the 16th International Conference on Knowledge Discovery and Data Mining, pp. 1029–1038 (2010)Google Scholar
  7. 7.
    Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proceedings of the 15th International Conference on Knowledge Discovery and Data Mining, pp. 199–208 (2009)Google Scholar
  8. 8.
    Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the 7th International Conference on Knowledge Discovery and Data Mining, pp. 57–66 (2001)Google Scholar
  9. 9.
    Girdhar, N., Bharadwaj, K.K.: Signed social networks: a survey. In: Singh, M., Gupta, P., Tyagi, V., Sharma, A., Ören, T., Grosky, W. (eds.) ICACDS 2016. CCIS, vol. 721, pp. 326–335. Springer, Singapore (2017).  https://doi.org/10.1007/978-981-10-5427-3_35CrossRefGoogle Scholar
  10. 10.
    Golbeck, J., Hendler, J.: Inferring binary trust relationships in web-based social networks. ACM Trans. Internet Technol. (TOIT) 6(4), 497–529 (2006)CrossRefGoogle Scholar
  11. 11.
    Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, pp. 241–250 (2010)Google Scholar
  12. 12.
    Guha, R., Kumar, R., Raghavan, P., Tomkins, A.: Propagation of trust and distrust. In: Proceedings of the 13th International Conference on World Wide Web, pp. 403–412 (2004)Google Scholar
  13. 13.
    Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the 9th International Conference on Knowledge Discovery and Data Mining, pp. 137–146 (2003)Google Scholar
  14. 14.
    Krasnogor, N., Aragón, A., Pacheco, J.: Memetic algorithms. In: Alba, E., Martí, R. (eds.) Metaheuristic Procedures for Training Neutral Networks. Operations Research/Computer Science Interfaces Series, vol. 36, pp. 225–248. Springer, Boston (2006).  https://doi.org/10.1007/0-387-33416-5_11CrossRefGoogle Scholar
  15. 15.
    Leskovec, J., Huttenlocher, D., Kleinberg, J.: Signed networks in social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1361–1370 (2010)Google Scholar
  16. 16.
    Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: Proceedings of the 15th International Conference on Knowledge Discovery and Data Mining, pp. 807–816 (2009)Google Scholar
  17. 17.
    Xu, K., Guo, X., Li, J., Lau, R.Y., Liao, S.S.: Discovering target groups in social networking sites: an effective method for maximizing joint influential power. Electron. Commer. Res. Appl. 11(4), 318–334 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Computer and Systems SciencesJawaharlal Nehru UniversityNew DelhiIndia

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