Abstract
Interactions occur across social networks, and modeling interactions in dynamic social networks is a challenging research problem that has broad applications. By combining topology in mathematics with field theory in physics, topology potential, which sets up a virtual field via a topology space to reflect individual activities, local effects and preferential attachments in different interactions, has been proposed to model mutual effects between individuals on social networks. In this paper, we take into consideration not only the information of topology structure and content but also two factors, namely, individual mass and interaction strength. From the perspective of smooth evolution of social networks, we propose a method based on dynamic topology potential, which captures the correlations between different changing snapshots of a social network and can be used to model interactions dynamically, so as to quantify the effects of interactions between individuals on dynamic social networks. Finally, we utilize the dynamic topology potential method for user influence analysis, especially for influential user identification, and the experiment conducted on a real-world data set from AMiner demonstrates the feasibility and effectiveness of our method in terms of a measure for network robustness.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Xia, Z., Wang, X., Sun, X., et al.: a secure and dynamic multi-keyword ranked search scheme over encrypted cloud data. IEEE Trans. Parallel Distrib. Syst. 27, 340–352 (2016)
Berger-Wolf, T.Y., Saia, J.: A framework for analysis of dynamic social networks. In: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 523–528. ACM Press, New York (2006)
Benevenuto, F., Rodrigues, T., Cha, M., et al.: Characterizing user behavior in online social networks. In: 9th ACM SIGCOMM Internet Measurement Conference, pp. 49–62. ACM Press, New York (2009)
Christakis, N.A., Fowler, J.H.: Social contagion theory: examining dynamic social networks and human behavior. Stat. Med. 32, 556–577 (2013)
Jiang, J., Wilson, C., Wang, X., et al.: Understanding latent interactions in online social networks. ACM Trans. Web 7, 18 (2013)
Hu, J., Han, Y., Hu, J.: Topological potential: modeling node importance with activity and local effect in complex networks. In: 2nd International Conference on Computer Modeling and Simulation, vol. 2, pp. 411–415. IEEE Computer Society Press, New York (2010)
Golder, S.A., Wilkinson, D.M., Huberman, B.A.: Rhythms of social interaction: messaging within a massive online network. CoRR, abs/cs/0611137 (2006)
Wilson, C., Boe, B., Sala, A., et al.: User interactions in social networks and their implications. In: 4th ACM European Conference on Computer systems, pp. 205–218. ACM Press, New York (2009)
Viswanath, B., Mislove, A., Cha, M., et al.: On the evolution of user interaction in Facebook. In: 2nd ACM Workshop on Online Social Networks, pp. 37–42. ACM Press, New York (2009)
Macskassy, S.A.: On the study of social interactions in Twitter. In: 6th International AAAI Conference on Weblogs and Social Media, pp. 226–233. AAAI Press, Palo Alto (2012)
Yan, Q., Wu, L., Zheng, L.: Social network based microblog user behavior analysis. Phys. A 392, 1712–1723 (2013)
Wilson, C., Sala, A., Puttaswamy, K., et al.: Beyond social graphs: user interactions in online social networks and their implications. ACM Trans. Web 6, 17 (2012)
Musial, K., Kazienko, P.: Social networks on the Internet. WWW 16, 31–72 (2013)
Shriver, S.K., Nair, H.S., Hofstetter, R.: Social ties and user-generated content: evidence from an online social network. Manage. Sci. 59, 1425–1443 (2013)
Zeng, X., Wei, L.: Social ties and user content generation: evidence from flickr. Inf. Syst. Res. 24, 71–87 (2012)
Rabade, R., Mishra, N., Sharma, S.: Survey of influential user identification techniques in online social networks. In: Thampi, S.M., Abraham, A., Pal, S.K., Rodriguez, J.M.C. (eds.) ISI 2014. Advances in Intelligent Systems and Computing, vol. 235, pp. 359–370. Springer, Heidelberg (2014)
Ghosh, R., Lerman, K.: Predicting Influential Users in Online Social Networks. CoRR, abs/1005.4882 (2010)
Aral, S., Walker, D.: Identifying influential and susceptible members of social network. Science 337, 337–341 (2012)
Wang, C., Tang, J., Sun, J., et al.: Dynamic social influence analysis through time-dependent factor graphs. In: 2011 International Conference on Advances in Social Networks Analysis and Mining, pp. 239–246. IEEE Computer Society Press, New York (2011)
Sun, Q., Wang, N., Zhou, Y., et al.: Modeling for user interaction by influence transfer effect in online social networks. In: 39th Conference on Local Computer Networks, pp. 486–489. IEEE Computer Society Press, New York (2014)
Han, Y., Li, D., Wang, T.: Identifying different community members in complex networks based on topology potential. Front. Comput. Sci. Chi. 5, 87–99 (2011)
Newman, M., Barabsi, A.-L., Watts, D.J.: The Structure and Dynamics of Networks. Princeton University Press, Princeton (2006)
Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948)
Li, D., Du, Y.: Artificial Intelligence with Uncertainty. Chapman & Hall/CRC, London (2007)
Lin, Y.-R., Chi, Y., Zhu, S., et al.: Analyzing communities and their evolutions in dynamic social networks. ACM Trans. Knowl. Discov. Data 3, 8 (2009)
Tang, W., Zhuang, H., Tang, J.: Learning to infer social ties in large networks. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds.) ECML PKDD 2011, Part III. LNCS, vol. 6913, pp. 381–397. Springer, Heidelberg (2011)
Tang, J., Wu, S., Sun, J.: Confluence: conformity influence in large social networks. In: 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 347–355. ACM Press, New York (2013)
Xing, W., Ghorbani, A.: Weighted pagerank algorithm. In: 2nd Annual Conference on Communication Networks and Services Research, pp. 305–314. IEEE Computer Society Press, New York (2004)
Chen, D., Lü, L., Shang, M.S., et al.: Identifying influential nodes in complex networks. Phys. A 391, 1777–1787 (2012)
Schneider, C.M., Moreira, A.A., Andrade, J.S., et al.: Mitigation of malicious attacks on networks. Proc. Natl. Acad. Sci. U.S.A. 108, 3838–3841 (2011)
Iyer, S., Killingback, T., Sundaram, B., et al.: Attack robustness and centrality of complex networks. PLoS ONE 8, e59613 (2013)
Guo, P., Wang, J., Li, B., et al.: A variable threshold-value authentication architecture for wireless mesh networks. J. Internet Technol. 15, 929–936 (2014)
Acknowledgement
We greatly appreciate Professor Deyi Li’s constructive comments and useful suggestions as well as anonymous reviewers’ professional comments, which help us to improve the quality and readability of our paper.
This work is supported by the National Basic Research Program (973 Program) of China (Grant No. 2014CB340401) and the National Natural Science Foundation of China (Grant Nos. 61272111, 61273213, and 61305055).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Huang, L., Ma, Y., Liu, Y. (2016). Social Influence Analysis Based on Modeling Interactions in Dynamic Social Networks: A Case Study. In: Sun, X., Liu, A., Chao, HC., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2016. Lecture Notes in Computer Science(), vol 10040. Springer, Cham. https://doi.org/10.1007/978-3-319-48674-1_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-48674-1_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48673-4
Online ISBN: 978-3-319-48674-1
eBook Packages: Computer ScienceComputer Science (R0)