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Influence me! Predicting links to influential users

  • Ariel Monteserin
  • Marcelo G. Armentano
Social Media for Personalization and Search
  • 56 Downloads

Abstract

In addition to being in contact with friends, online social networks are commonly used as a source of information, suggestions and recommendations from members of the community. Whenever we accept a suggestion or perform any action because it was recommended by a “friend”, we are being influenced by him/her. For this reason, it is useful for users seeking for interesting information to identify and connect to this kind of influential users. In this context, we propose an approach to predict links to influential users. Compared to approaches that identify general influential users in a network, our approach seeks to identify users who might have some kind of influence to individual (target) users. To carry out this goal, we adapted an influence maximization algorithm to find new influential users from the set of current influential users of the target user. Moreover, we compared the results obtained with different metrics for link prediction and analyzed in which context these metrics obtained better results.

Keywords

Link prediction Social influence Social networks 

Notes

Acknowledgements

This research was partially supported by ANPCyT through PICT Project No. 2014-2750.

References

  1. Anderson, A., Huttenlocher, D., Kleinberg, J., & Leskovec, J. (2012). Effects of user similarity in social media. In Proceedings of the fifth ACM international conference on web search and data mining, WSDM ’12 (pp. 703–712). New York, NY: ACM.Google Scholar
  2. Aral, S., Muchnik, L., & Sundararajan, A. (2009). Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences, 106(51), 21544–21549.CrossRefGoogle Scholar
  3. Armentano, M. G., Godoy, D., & Amandi, A. (2012). Topology-based recommendation of users in micro-blogging communities. Journal of Computer Science and Technology, 27(3), 624–634.CrossRefGoogle Scholar
  4. Armentano, M. G., Godoy, D., & Amandi, A. A. (2013). Followee recommendation based on text analysis of micro-blogging activity. Information Systems, 38(8), 1116–1127.CrossRefGoogle Scholar
  5. Bakshy, E., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011). Everyone’s an influencer: Quantifying influence on twitter. In Proceedings of the fourth ACM international conference on web search and data mining, WSDM ’11 (pp. 65–74). New York, NY: ACM.Google Scholar
  6. Bhattacharyya, P., Garg, A., & Wu, S. F. (2010). Analysis of user keyword similarity in online social networks. Social Network Analysis and Mining, 1(3), 143–158.CrossRefGoogle Scholar
  7. Bonchi, F. (2011). Influence propagation in social networks: A data mining perspective. In 2011 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology (WI-IAT) (Vol. 1, p. 2).Google Scholar
  8. Chen, H.-H., Gou, L., Zhang, X. L., & Giles, C. L. (2012). Discovering missing links in networks using vertex similarity measures. In Proceedings of the 27th annual ACM symposium on applied computing (pp. 138–143).Google Scholar
  9. Chiang, K.-Y., Natarajan, N., Tewari, A., & Dhillon, I. S. (2011). Exploiting longer cycles for link prediction in signed networks. In Proceedings of the 20th ACM international conference on information and knowledge management (pp. 1157–1162).Google Scholar
  10. Choudhury, N., & Uddin, S. (2017). Evolution similarity for dynamic link prediction in longitudinal networks. In B. Gonçalves, R. Menezes, R. Sinatra, & V. Zlatic (Eds.), Complex Networks VIII (pp. 109–118). Cham: Springer.CrossRefGoogle Scholar
  11. Choudhury, N., & Uddin, S. (2018). Evolutionary community mining for link prediction in dynamic networks. In C. Cherifi, H. Cherifi, M. Karsai, & M. Musolesi (Eds.), Complex Networks and Their Applications VI (pp. 127–138). Cham: Springer.CrossRefGoogle Scholar
  12. Dai, C., Chen, L., & Li, B. (2017). Network link prediction based on direct optimization of area under curve. Applied Intelligence, 46(2), 427–437.CrossRefGoogle Scholar
  13. De Domenico, M., Lima, A., Mougel, P., & Musolesi, M. (2013). The anatomy of a scientific rumor. Scientific Reports, 3, 02980.CrossRefGoogle Scholar
  14. Ding, J., Jiao, L., Jianshe, W., & Liu, F. (2016). Prediction of missing links based on community relevance and ruler inference. Knowledge-Based Systems, 98, 200–215.CrossRefGoogle Scholar
  15. Fouss, F., Pirotte, A., Renders, J.-M., & Saerens, M. (2007). Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Transactions on Knowledge and Data Engineering, 19(3), 355–369.CrossRefGoogle Scholar
  16. Goyal, A. (2013). Social influence and its applications: An algorithmic and data mining study. Ph.D. thesis, University of British Columbia.Google Scholar
  17. Goyal, A., Bonchi, F., & Lakshmanan, L. V. S. (2010). Learning influence probabilities in social networks. In Proceedings of the third ACM international conference on web search and data mining, WSDM ’10 (pp. 241–250). New York, NY: ACM.Google Scholar
  18. Goyal, A., Bonchi, F., & Lakshmanan, L. V. S. (2011). A data-based approach to social influence maximization. PVLDB, 5(1), 73–84.Google Scholar
  19. Goyal, A., Lu, W., & Lakshmanan, L. V. S. (2011). Celf++: Optimizing the greedy algorithm for influence maximization in social networks. In 20th international world wide web conference on proceedings of WWW 2011 (pp. 47–48).Google Scholar
  20. Güneş, İ., Gündüz-Öğüdücü, Ş., & Çataltepe, Z. (2016). Link prediction using time series of neighborhood-based node similarity scores. Data Mining and Knowledge Discovery, 30(1), 147–180.MathSciNetCrossRefGoogle Scholar
  21. Ienco, D., Bonchi, F., & Castillo, C. (2010). The meme ranking problem: Maximizing microblogging virality. In 2010 IEEE international conference on data mining workshops (ICDMW) (pp. 328–335).Google Scholar
  22. Jamali, M., & Ester, M. (2010). A matrix factorization technique with trust propagation for recommendation in social networks. In Proceedings of the fourth ACM conference on recommender systems, RecSys ’10 (pp. 135–142). New York, NY: ACM.Google Scholar
  23. Jiang, J., Shi, P., An, B., Jianyong, Y., & Wang, C. (2017). Measuring the social influences of scientist groups based on multiple types of collaboration relations. Information Processing & Management, 53(1), 1–20.CrossRefGoogle Scholar
  24. Katz, L. (1953). A new status index derived from sociometric analysis. Psychometrika, 18(1), 39–43.CrossRefzbMATHGoogle Scholar
  25. Kempe, D., Kleinberg, J., & Tardos, É.(2003). Maximizing the spread of influence through a social network. In Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’03 (pp. 137–146). New York, NY: ACM.Google Scholar
  26. La Fond, T., & Neville, J. (2010). Randomization tests for distinguishing social influence and homophily effects. In Proceedings of the 19th international conference on world wide web, WWW ’10 (pp. 601–610). New York, NY: ACM.Google Scholar
  27. Liben-Nowell, D., & Kleinberg, J. (2003). The link prediction problem for social networks. In Proceedings of the 12th international conference on information and knowledge management (CIKM).Google Scholar
  28. Li, X., & Chen, H. (2013). Recommendation as link prediction in bipartite graphs. Decision Support Systems, 54(2), 880–890.CrossRefGoogle Scholar
  29. Liu, D., Wang, L., Zheng, J., Ning, K., & Zhang, L.-J. (2013). Influence analysis based expert finding model and its applications in enterprise social network. In 2013 IEEE international conference on services computing (Vol. 0, pp. 368–375).Google Scholar
  30. Lü, L., Jin, C.-H., & Zhou, T. (2009). Similarity index based on local paths for link prediction of complex networks. Physical Review E, 80(4), 046122.CrossRefGoogle Scholar
  31. Lü, L., & Zhou, T. (2011). Link prediction in complex networks: A survey. Physica A, 390(6), 11501170.CrossRefGoogle Scholar
  32. Monteserin, A., & Amandi, A. (2015). Whom should I persuade during a negotiation? An approach based on social influence maximization. Decision Support Systems, 77, 1–20.CrossRefGoogle Scholar
  33. Monteserin, A., & Armentano, M. G. (2018). Influence-based approach to market basket analysis. Information Systems (in press).Google Scholar
  34. Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The pagerank citation ranking: Bringing order to the web. Technical report, Stanford InfoLab.Google Scholar
  35. Perlich, C., Swirszcz, G., & Lawrence, R. (2009). Content-based link prediction for patent marketing. Technical report, IBM Research Division.Google Scholar
  36. Pobiedina, N., & Ichise, R. (2016). Citation count prediction as a link prediction problem. Applied Intelligence, 44(2), 252–268.CrossRefGoogle Scholar
  37. Rahman, M., & Hasan, M. A. (2016). Link prediction in dynamic networks using graphle. In P. Frasconi, N. Landwehr, G. Manco, & J. Vreeken (Eds.), Machine Learning and Knowledge Discovery in Databases (pp. 394–409). Cham: Springer.CrossRefGoogle Scholar
  38. Rashotte, L. (2007). Social influence. In G. Ritzer (Ed.), Blackwell encyclopedia of sociology (Vol. IX, pp. 4426–4429). London: Blackwell.Google Scholar
  39. Scellato, S., Noulas, A., & Mascolo, C. (2011). Exploiting place features in link prediction on location-based social networks. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (pp 1046–1054). ACM.Google Scholar
  40. Scripps, J., Tan, P.-N., Chen, F., & Esfahanian, A.-H. (2008). A matrix alignment approach for link prediction. In ICPR (pp. 1–4).Google Scholar
  41. Wang, Y., Wang, L., Li, Y., He, D., Liu, T.-Y., & Chen, W. (2013). A theoretical analysis of NDCG type ranking measures. CoRR arXiv:1304.6480.
  42. Wang, P., BaoWen, X., YuRong, W., & Zhou, X. Y. (2015). Link prediction in social networks: The state-of-the-art. Science China Information Sciences, 58(1), 1–38.Google Scholar
  43. Wortman, J. (2008). Viral marketing and the diffusion of trends on social networks. Technical Report No. MS-CIS-08-19, University of Pennsylvania Department of Computer and Information Science.Google Scholar
  44. Ye, M., Liu, X., & Lee, W.-C. (2012). Exploring social influence for recommendation: A generative model approach. In Proceedings of the 35th international ACM SIGIR conference on research and development in information retrieval, SIGIR ’12 (pp. 671–680). New York, NY: ACM.Google Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.ISISTAN (CONICET/UNICEN)TandilArgentina

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