Abstract
Online Social Networks (OSNs) are becoming popular and attracting lots of participants. In OSN based e-commerce platforms, a buyer’s review of a product is one of the most important factors for other buyers’ decision makings. A buyer who provides high quality reviews thus has strong social influence, and can impact a large number of participants’ purchase behaviours in OSNs. However, the dishonest participants can cheat the existing social influence evaluation models by using some typical attacks, like Constant and Camouflage, to obtain fake strong social influence. Therefore, it is significant to accurately evaluate such social influence to recommend the participants who have strong social influences and provide high quality product reviews. In this paper, we propose an Evolutionary-Based Robust Social Influence (EB-RSI) method based on the trust evolutionary models. In our EB-RSI, we propose four influence impact factors in social influence evaluation, i.e., Total Trustworthiness (TT), Fluctuant Trend of Being Advisor (FTBA), Fluctuant Trend of Trustworthiness (FTT) and Trustworthiness Area (TA). They are all significant in the influence evaluation. We conduct experiments onto a real social network dataset Epinions, and validate the effectiveness and robustness of our EB-RSI by comparing with state-of-the-art method, SoCap. The experimental results demonstrate that our EB-RSI can more accurately evaluate participants’ social influence than SoCap.
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References
Akoglu, L., Chandy, R., Faloutsos, C.: Opinion fraud detection in online reviews by network effects. In: ICWSM (2013)
Au Yeung, C.M., Iwata, T.: Strength of social influence in trust networks in product review sites. In: WSDM, pp. 495–504 (2011)
Bedi, P., Kaur, H., Marwaha, S.: Trust based recommender system for semantic web. In: IJCAI, pp. 2677–2682 (2007)
Berscheid, E., Reis, H.T., et al.: Attraction and close relationships. The Handbook of Social Psychology 2, 193–281 (1998)
Chen, W., Lu, W., Zhang, N.: Time-critical influence maximization in social networks with time-delayed diffusion process. In: AAAI, pp. 592–598 (2012)
Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: KDD, pp. 1029–1038 (2010)
Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: KDD, pp. 199–208 (2009)
Chen, W., Paik, I., Wang, J., Kumara, B.T., Tanaka, T.: Awareness of social influence on linked social service. In: 2013 IEEE International Conference on Cybernetics (CYBCONF), pp. 32–39 (2013)
Cho, Y.S., Ver Steeg, G., Galstyan, A.: Co-evolution of selection and influence in social networks. In: AAAI (2011)
Fiske, S.T.: Social beings: Core motives in social psychology. John Wiley & Sons (2009)
Franks, H., Griffiths, N., Anand, S.S.: Learning influence in complex social networks. In: AAMAS, pp. 447–454 (2013)
Han, J., Kamber, M.: Data Mining Concepts and Techniques: Data Preprocessing. Diane Cerra (2006)
Jiang, S., Zhang, J., Ong, Y.S.: An evolutionary model for constructing robust trust networks. In: AAMAS, pp. 813–820 (2013)
Jøsang, A., Ismail, R., Boyd, C.: A survey of trust and reputation systems for online service provision. Decision Support Systems 43(2), 618–644 (2007)
Jung, K., Heo, W., Chen, W.: Irie: Scalable and robust influence maximization in social networks. In: ICDM, pp. 918–923 (2012)
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: KDD, pp. 137–146 (2003)
Kim, J., Kim, S.K., Yu, H.: Scalable and parallelizable processing of influence maximization for large-scale social networks? In: ICDE, pp. 266–277 (2013)
Kimura, M., Saito, K.: Tractable models for information diffusion in social networks. In: PKDD, pp. 259–271 (2006)
Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: KDD, pp. 420–429 (2007)
Li, L., Wang, Y.: A trust vector approach to service-oriented applications. In: ICWS, pp. 270–277 (2008)
Okelo, B., Boston, S., Minchev, D.: Advanced Mathematics: The Differential Calculus for Multi-variable Functions. LAP Lambert Academic (2012)
Pandit, S., Chau, D.H., Wang, S., Faloutsos, C.: Netprobe: a fast and scalable system for fraud detection in online auction networks. In: Proceedings of the 16th International Conference on World Wide Web, pp. 201–210 (2007)
Subbian, K., Sharma, D., Wen, Z., Srivastava, J.: Finding influencers in networks using social capital. In: ASONAM, pp. 592–599 (2013)
Yaniv, I.: Receiving other people’ s advice: Influence and benefit. Organizational Behavior and Human Decision Processes 93(1), 1–13 (2004)
Zhang, J., Liu, B., Tang, J., Chen, T., Li, J.: Social influence locality for modeling retweeting behaviors. In: AAAI, pp. 2761–2767 (2013)
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Zhu, F., Liu, G., Liu, A., Zhao, L., Zhou, X. (2014). An Evolution-Based Robust Social Influence Evaluation Method in Online Social Networks. In: Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2014. WISE 2014. Lecture Notes in Computer Science, vol 8787. Springer, Cham. https://doi.org/10.1007/978-3-319-11746-1_10
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DOI: https://doi.org/10.1007/978-3-319-11746-1_10
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