Abstract
Viral marketing campaigns are often negatively affected by overexposure. Overexposure occurs when users become less likely to favor a promoted product after receiving information about the product from too large a fraction of their friends. Yet, existing influence diffusion models do not take overexposure into account, effectively overestimating the number of users who favor the product and diffuse information about it. In this work, we propose the first influence diffusion model that captures overexposure. In our model, Latency Aware Independent Cascade Model with Overexposure (LAICO), the activation probability of a node representing a user is multiplied (discounted) by an overexposure score, which is calculated based on the ratio between the estimated and the maximum possible number of attempts performed to activate the node. We also study the influence maximization problem under LAICO. Since the spread function in LAICO is non-submodular, algorithms for submodular maximization are not appropriate to address the problem. Therefore, we develop an approximation algorithm that exploits monotone submodular upper and lower bound functions of spread, and a heuristic that aims to maximize a proxy function of spread iteratively. Our experiments show the effectiveness and efficiency of our algorithms.
- A. Awan. 2015. Less is more: You are about to receive less email from LinkedIn. Retrieved from https://blog.linkedin.com/2015/07/27/less-email-from-linkedin?sf11404748=1.Google Scholar
- R. Abebe, L. A. Adamic, and J. Kleinberg. 2018. Mitigating overexposue in viral marketing. In AAAI.Google Scholar
- F. Alkemade and C. Castaldi. 2005. Strategies for the diffusion of innovations on social networks. Comp. Econ. 25, 1 (2005).Google Scholar
- N. Bhatti, A. Bouch, and A. Kuchinsky. 2000. Integrating user-perceived quality into web server design. Comput. Netw. 33, 1--6 (2000), 1--16.Google Scholar
Digital Library
- J. A. Bilmes and W. Bai. 2017. Deep submodular functions. CoRR abs/1701.08939 (2017).Google Scholar
- K. Bontcheva, G. Gorrell, and B. Wessels. 2013. Social media and information overload. CoRR abs/1306.0813 (2013).Google Scholar
- C. Borgs, M. Brautbar, J. Chayes, and B. Lucier. 2014. Maximizing social influence in nearly optimal time. In SODA.Google Scholar
- N. Buchbinder, M. Feldman, J. Naor, and R. Schwartz. 2014. Submodular maximization with cardinality constraints. In SODA.Google Scholar
- W. Chen, W. Lu, and N. Zhang. 2012. Time-critical influence maximization in social networks with time-delayed diffusion process. In AAAI.Google Scholar
- W. Chen, C. Wang, and Y. Wang. 2010. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In KDD. 1029--1038.Google Scholar
- W. Chen, Y. Wang, and S. Yang. 2009. Efficient influence maximization in social networks. In KDD. 199--208.Google Scholar
- P. Dagum, R. Karp, M. Luby, and S. Ross. 2000. An optimal algorithm for monte carlo estimation. SIAM J. Comput. 29, 5 (2000), 1484--1496.Google Scholar
Digital Library
- S. Feng, X. Chen, G. Cong, Y. Zeng, Y. M. Chee, and Y. Xiang. 2014. Influence maximization with novelty decay in social networks. In AAAI.Google Scholar
- J. Goldenberg, B. Libai, and E. Muller. 2001. Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Lett. 12 (2001), 211--223.Google Scholar
Cross Ref
- Mark Granovetter. 1978. Threshold models of collective behavior. Amer. J. Sociology 83, 6 (1978), 1420--1443.Google Scholar
Cross Ref
- R. Gwadera and G. Loukides. 2017. Cost-effective viral marketing in the latency aware independent cascade model. In PAKDD. 251--265.Google Scholar
- R. Iyer and J. Bilmes. 2012. Algorithms for approximate minimization of the difference between submodular functions, with applications. In UAI.Google Scholar
- J. Jeong and S. Moon. 2014. Invite your friends and get rewards: Dynamics of incentivized friend invitation in kakaotalk mobile games. In ACM COSN.Google Scholar
- K. Kalyanam, S. McIntyre, and T. J. Masonis. 2007. Adaptive experimentation in interactive marketing: The case of viral marketing at Plaxo. J. of Int. Marketing 21, 3 (2007), 72--85.Google Scholar
- D. Kempe, J. Kleinberg, and E. Tardos. 2003. Maximizing the spread of influence through a social network. In KDD.Google Scholar
- M. Kimura, K. Saito, and H. Motoda. 2009. Blocking links to minimize contamination spread in a social network. ACM Trans. Knowl. Discov. Data 3, 2 (2009).Google Scholar
Digital Library
- A. Krause and D. Golovin. 2013. Submodular function maximization. In Tractability.Google Scholar
- J. Leskovec, L. A. Adamic, and B. A. Huberman. 2007. The dynamics of viral marketing. ACM Trans. Web 1, 1 (May 2007), 5--es.Google Scholar
Digital Library
- Y. Li, J. Fan, Y. Wang, and K. Tan. 2018. Influence maximization on social graphs: A survey. TKDE 30, 10 (2018), 1852--1872.Google Scholar
Cross Ref
- B. Liu, G. Cong, D. Xu, and Y. Zeng. 2012. Time constrained influence maximization in social networks. In ICDM.Google Scholar
- G. Loukides and R. Gwadera. 2018. Preventing the diffusion of information to vulnerable users while preserving PageRank. Int. J. Data Sci. Anal. 5, 1 (2018), 19--39.Google Scholar
Cross Ref
- J. J. Louviere, D. A. Hensher, and J. D. Swait. 2000. Stated Choice Methods Analysis and Applications.Google Scholar
- W. Lu, W. Chen, and L. V. S. Lakshmanan. 2015. From competition to complementarity: Comparative influence diffusion and maximization. PVLDB 9, 2 (2015).Google Scholar
Digital Library
- M. Minoux. 1978. Accelerated greedy algorithms for maximizing submodular set functions. In Optimization Techniques.Google Scholar
- M. Mitrovic, M. Bun, A. Krause, and A. Karbasi. 2017. Differentially private submodular maximization: Data summarization in disguise. In ICML.Google Scholar
- S. A. Myers, C. Zhu, and J. Leskovec. 2012. Information diffusion and external influence in networks. In KDD. 33--41.Google Scholar
- G. L. Nemhauser, L. A. Wolsey, and M. L. Fisher. 1978. An analysis of approximations for maximizing submodular set functions. Math. Program. 14, 1 (1978), 265--294.Google Scholar
Digital Library
- N. Ohsaka, Y. Yamaguchi, N. Kakimura, and K. Kawarabayashi. 2016. Maximizing time-decaying influence in social networks. In ECML/PKDD.Google Scholar
- Ayse Bengi Ozcelik and Kaan Varnali. 2019. Effectiveness of online behavioral targeting: A psychological perspective. Electron. Commerce Res. Appl. 33 (2019), 100819.Google Scholar
Cross Ref
- C. C. Pugh. 2015. Real Mathematical Analysis.Google Scholar
- D. M. Romero, B. Meeder, and J. Kleinberg. 2011. Differences in the mechanics of information diffusion across topics. In WWW.Google Scholar
- D. Simchi-Levi, X. Chen, and J. Bramel. 2005. Convexity and Supermodularity. Springer New York.Google Scholar
- Z. Svitkina and L. Fleischer. 2011. Submodular approximation: Sampling-based algorithms and lower bounds. SIAM J. Comput. 40, 6 (2011), 1715--1737.Google Scholar
Digital Library
- Y. Tang, Y. Shi, and X. Xiao. 2015. Influence maximization in near-linear time: A martingale approach. In SIGMOD.Google Scholar
- A. Volkova. 1996. A refinement of the central limit theorem for sums of independent random indicators. Theory Probab. Appl. 40, 4 (1996), 791--794.Google Scholar
Cross Ref
- B. Wang, G. Chen, L. Fu, L. Song, and X. Wang. 2017. DRIMUX: Dynamic rumor influence minimization with user experience in social networks. IEEE Trans. Knowl. Data Eng. 29, 10 (2017), 2168--2181.Google Scholar
Digital Library
- L. Wu, P. Sun, Y. Fu Yanjie, R. Hong, X. Wang, and M. Wang. 2019. A neural influence diffusion model for social recommendation. In SIGIR. 235--244.Google Scholar
- Y. Zhou and L. Liu. 2013. Social influence based clustering of heterogeneous information networks. In KDD. 338--346.Google Scholar
- J. Zhu, J. Zhu, S. Ghosh, W. Wu, and J. Yuan. 2019. Social influence maximization in hypergraph in social networks. IEEE Trans. Network Sci. Eng. 6, 4 (2019).Google Scholar
Cross Ref
- Y. Zhu, P. Yin, D. Li, and B. Lin. 2019. Strengthening the positive effect of viral marketing. In ICDCS. 1941--1950.Google Scholar
Index Terms
Overexposure-Aware Influence Maximization
Recommendations
FastCELF++: A Novel and Fast Heuristic for Influence Maximization in Complex Networks
Computational Science and Its Applications – ICCSA 2023AbstractSocial networks reflect the relationships and interactions between individuals and have played a significant role in the spread of information, in which the communication of ideas and sharing of opinions happen all the time. There are various ...
Influence Maximization in Online Social Networks
WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data MiningStarting with the earliest studies showing that the spread of new trends, information, and innovations is closely related to the social influence exerted on people by their social networks, the research on social influence theory took off, providing ...
Two evidential data based models for influence maximization in Twitter
An evidential influence measure for Twitter was proposed.Many influence aspects were considered in the proposed measure.Two evidential influence maximization models were introduced.The performance of the proposed models compared to existing ones was ...






Comments