Abstract
Information diffusion has been studied for many years to understand how information diffuse in social network or real world. However, which nodes and when they will get influenced are unpredictable because of the uncertainty of information diffusion even we know the initial influenced nodes and diffusion network. Verification is the only way to make sure if a node is influenced or not. The target of discovering influenced nodes is to find more influenced nodes under the limited amount of verifications. In this paper, the temporal contact network is modeled. Then influenced nodes discovery problem in temporal contact network are studied based on the Independent Cascade (IC) model. A path length limited approach is proposed to calculate the infection probability approximately. Experimental results on real and synthetic data sets show our approach has better performance than BFS and Random Walk algorithm.
This is a preview of subscription content, log in via an institution to check access.
References
Abrahao, B., Chierichetti, F., Kleinberg, R., Panconesi, A.: Trace complexity of network inference. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 491–499. ACM (2013)
Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1029–1038. ACM (2010)
Du, N., Liang, Y., Balcan, M.-F., Song, L.: Influence function learning in information diffusion networks. In: Proceedings of the 31st International Conference on Machine Learning, vol. 2014, p. 2016. NIH Public Access (2014)
Gomez Rodriguez, M., Balduzzi, D., Schölkopf, B., Scheffer, G.T. et al.: Uncovering the temporal dynamics of diffusion networks. In: 28th International Conference on Machine Learning (ICML 2011), pp. 561–568. International Machine Learning Society (2011)
Gomez Rodriguez, M., Leskovec, J., Krause, A.: Inferring networks of diffusion and influence. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1019–1028. ACM (2010)
Guille, A., Hacid, H., Favre, C., Zighed, D.A.: Information diffusion in online social networks: a survey. ACM SIGMOD Rec. 42(2), 17–28 (2013)
Huang, S., Cheng, J., Wu, H.: Temporal graph traversals: definitions, algorithms, and applications. arXiv preprint arXiv:1401.1919 (2014)
Iwata, T., Shah, A., Ghahramani, Z.: Discovering latent influence in online social activities via shared cascade poisson processes. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 266–274 (2013)
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM (2003)
Kutzkov, K., Bifet, A., Bonchi, F., Gionis, A.: STRIP: stream learning of influence probabilities. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 275–283. ACM (2013)
Leskovec, J., Faloutsos, C.: Sampling from large graphs. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 631–636 (2006)
Mehdiabadi, M.E., Rabiee, H.R., Salehi, M.: Sampling from diffusion networks. In: Proceedings of the 2012 International Conference on Social Informatics, pp. 106–112. IEEE Computer Society (2012)
Myers, S.A., Zhu, C., Leskovec, J.: Information diffusion and external influence in networks. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 33–41. ACM (2012)
Najar, A., Denoyer, L., Gallinari, P.: Predicting information diffusion on social networks with partial knowledge. In: Proceedings of the 21st International Conference Companion on World Wide Web, pp. 1197–1204. ACM (2012)
Tang, J., Sun, J., Wang, C., Yang, Z.: Social influence analysis in large-scale networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 807–816. ACM (2009)
Tang, Y., Shi, Y., Xiao, X.: Influence maximization in near-linear time: a martingale approach. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 1539–1554. ACM (2015)
Valiant, L.G.: The complexity of enumeration and reliability problems. SIAM J. Comput. 8, 410–421 (1979)
Wu, H., Cheng, J., Huang, S., Ke, Y., Lu, Y., Xu, Y.: Path problems in temporal graphs. Proc. VLDB Endow. 7(9), 721–732 (2014)
Zhang, J., Wang, C., Wang, J., Yu, J.X.: Inferring continuous dynamic social influence and personal preference for temporal behavior prediction. Proc. VLDB Endow. 8(3), 269–280 (2014)
Zhang, M., Dai, C., Ding, C., Chen, E.: Probabilistic solutions of influence propagation on social networks. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, pp. 429–438. ACM (2013)
Acknowledgement
This work was supported by the National Natural Science Foundation of China under Grant Nos. 61572335 and 61572336, the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20151223, the Natural Science Foundation of Jiangsu Provincial Department of Education of China under Grant No. 12KJB520017, and Collaborative Innovation Center of Novel Software Technology and Industrialization, Jiangsu, China.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Huang, J., Lin, T., Liu, A., Li, Z., Yin, H., Zhao, L. (2017). Influenced Nodes Discovery in Temporal Contact Network. In: Bouguettaya, A., et al. Web Information Systems Engineering – WISE 2017. WISE 2017. Lecture Notes in Computer Science(), vol 10569. Springer, Cham. https://doi.org/10.1007/978-3-319-68783-4_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-68783-4_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68782-7
Online ISBN: 978-3-319-68783-4
eBook Packages: Computer ScienceComputer Science (R0)