Abstract
How to advertise in online social networks is a hot and open research topic. In short, the main goal is to post the advertisement on a few of most influential users’ profiles to spread the advertising information to the potential suitable recipients. Typical research works about this topic are limitedly involved with two aspects: one is Spreading Maximization problem and the other is Centrality measures. The Spreading Maximization is proven to be an NP-hard problem, which means the corresponding method is mostly inefficient to exactly find the most influential spreaders. Second, traditional centrality measures, such as degree centrality, closeness centrality etc., roughly take the geometric information (degree or distance) to calculate the potentially most influential users, rather than considering online users’ personal interests or preference which are more likely to determine the set of people whether read/accept the advertising contents or not. In this paper, we put closed-related labels for each individual’s profile in the online social network and assign particularly set-up scores to these attribute labels. Based on these labels, we apply the weighted k-shell decomposition method to identify the core users in the networks, which is also regarded as the most influential users in this paper. The experimental results show that the proposed method is sufficient to identify the most influential users in some artificial networks. More importantly, the proposed method shows good discrimination degree of influence ranking.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Armstrong, G.: Marketing: An Introduction. Pearson Education, New York (2009)
Hays, L.: Technology: Using Computers to Divine Who Might Buy a Gas Grill. Wall Street Journal, New York (1994)
Sun, F., Shi, Y., Wang, W.: Content-based recommendation system based on vague sets, 294–297. IHMSC (2013)
Fang, P., Zheng, S.: A research on fuzzy formal concept analysis based collaborative filtering recommendation system. KAM, 352–355 (2009)
Domingos, P., Richardson, M.: Mining the network value of customers. ACM SIGKDD 57–66 (2001)
Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. ACM SIGKDD, 61–70 (2002)
Chen, D., Lv, L., Shang, M., Zhang, Y., Zhou, T.: Identifying influential nodes in complex networks. Phys. A 391(4), 1777–1787 (2012)
Jordán, F., Benedek, Z., Podani, J.: Quantifying positional importance in food webs: a comparison of centrality indices. Ecol. Model. 205(1–2), 270–275 (2007)
Kolaczyk, E.D., Chua, D.B., Barthlemy, M.: Group betweenness and co-betweenness: inter-related notions of coalition centrality. Soc. Net. 31(3), 190–203 (2009)
Wang, F., Antipova, A., Porta, S.: Street centrality and land use intensity in Baton Rouge, Louisiana. J. Transp. Geogr. 19(2), 285–293 (2011)
Zhang, H., Fiszman, M., Shin, D., et al.: Degree centrality for semantic abstraction summarization of therapeutic studies. J. Biomed. Inform. 44(5), 830–838 (2011)
Linyuan, L., Jin, C.H., Zhou, T.: Similarity index based on local paths for link prediction of complex networks. Phys. Rev. E 80(4), 046122 (2009)
Wang, J., Rong, L.: Similarity index based on the information of neighbor nodes for link prediction of complex network. Mod. Phys. Lett. B 27(06), 1350039 (2013)
Lü, L., Zhou, T.: Link prediction in weighted networks: the role of weak ties. Europhys. Lett. 89(1), 18001 (2010)
Pathak, P.H., Dutta, R.: Centrality-based power control for hot-spot mitigation in multi-hop wireless networks. Comput. Commun. 35(9), 1074–1085 (2012)
Kitsak, M., Gallos, L.K., Havlin, S., et al.: Identification of influential spreaders in complex networks. Nat. Phys. 6(11), 888–893 (2010)
Fan, W.L., Liu, Z.G., Hu, P.: A high robustness and low cost cascading failure model based on node importance in complex networks. Mod. Phys. Lett. B 28(02), 1450011 (2014)
Zhang, Z.: Information entropy of diffusion processes on complex networks. Mod. Phys. Lett. 28(17), 1450141 (2014)
Wei, D., Deng, X., Zhang, X., et al.: Identifying influential nodes in weighted networks based on evidence theory. Phys. A 392(10), 2564–2575 (2013)
Nicosia, V., Criado, R., Romance, M., et al.: Controlling centrality in complex networks. Sci. Rep. 2, 218–223 (2012)
Opsahl, T., Agneessens, F., Skvoretz, J.: Node centrality in weighted networks: generalizing degree and shortest paths. Soc. Net. 32(3), 245–251 (2010)
Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Net. 1(3), 215–239 (1978)
Bonacich, P., Lloyd, P.: Calculating status with negative relations. Soc. Net. 26(4), 331–338 (2004)
Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)
Estrada, E., Rodrguez-Velzquez, J.A.: Subgraph centrality in complex networks. Phys. Rev. E 71(5), 056103 (2005)
Brin, S., Page, L.: The pagerank citation ranking: bringing order to the web. Comput. Netw. 107–117 (1998)
Lv, L., Zhou, T.: Leaders in social networks, the delicious case. PloS ONE 6(6), e21202 (2011)
Newman, M.: Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Phys. Rev. E 64(1), 016132 (2001)
Newman, M.: Analysis of weighted networks. Phys. Rev. E 70(5), 056131 (2004)
Chu, X., Zhang, Z., Guan, J., et al.: Epidemic spreading with nonlinear infectivity in weighted scale-free networks. Phys. A 390(3), 471–481 (2009)
Garas, A., Schweitzer, F., Havlin, S.: AK-shell decomposition method for weighted networks. New Phys. 14(8), 083030 (2012)
Zhou, T., Liu, J.G., Bai, W.J., et al.: Behaviors of susceptible-infected epidemics on scale-free networks with identical infectivity. Phys. Rev. E 74(5), 056109 (2006)
Yan, G., Zhou, T., Wang, J., et al.: Epidemic spread in weighted scale-free networks. Chin. Phys. Lett. 22(2), 510–513 (2005)
Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33(4), 452–473 (1977)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Zou, X., Liu, H., Dai, X., Xiong, J., Zhang, N. (2020). An Efficient Method of Advertising on Online Social Networks. In: Han, M., Qin, S., Zhang, N. (eds) Advances in Neural Networks – ISNN 2020. ISNN 2020. Lecture Notes in Computer Science(), vol 12557. Springer, Cham. https://doi.org/10.1007/978-3-030-64221-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-64221-1_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-64220-4
Online ISBN: 978-3-030-64221-1
eBook Packages: Computer ScienceComputer Science (R0)