Abstract
Finding a set of nodes that maximizes the spread in a network, known as the influence maximization problem, has been addressed from multiple angles throughout the literature. Traditional solutions focus on the algorithmic aspect of the problem and are based solely on static networks. However, with the emergence of several complementary data, such as the network’s temporal changes and the diffusion cascades taking place over it, novel methods have been proposed with promising results. Here, we introduce a simple yet effective algorithm that combines the algorithmic methodology with the diffusion cascades. We compare it with four different prevalent influence maximization approaches, on a large scale Chinese microblogging dataset. More specifically, for comparison, we employ methods that derive the seed set using the static network, the temporal network, the diffusion cascades, and their combination. A set of diffusion cascades from the latter part of the dataset is set aside for evaluation. Our method outperforms the rest in both quality of the seed set and computational efficiency.
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References
Bourigault, S., Lamprier, S., Gallinari, P.: Representation learning for information diffusion through social networks: an embedded cascade model. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pp. 573–582. ACM (2016)
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, P.K., et al.: Measuring user influence in twitter: the million follower fallacy. ICWSM 10(10–17), 30 (2010)
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)
Cohen, E., Delling, D., Pajor, T., Werneck, R.F.: Sketch-based influence maximization and computation: scaling up with guarantees. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 629–638. ACM (2014)
Du, N., Song, L., Rodriguez, M.G., Zha, H.: Scalable influence estimation in continuous-time diffusion networks. In: Advances in Neural Information Processing Systems, pp. 3147–3155 (2013)
Gallos, L.K., Song, C., Makse, H.A.: Scaling of degree correlations and its influence on diffusion in scale-free networks. Phys. Rev. Lett. 100(24), 248,701 (2008)
Goyal, A., Bonchi, F., Lakshmanan, L.V.: Learning influence probabilities in social networks. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 241–250. ACM (2010)
Goyal, A., Bonchi, F., Lakshmanan, L.V.: A data-based approach to social influence maximization. Proc. VLDB Endow. 5(1), 73–84 (2011)
Goyal, A., Lu, W., Lakshmanan, L.V.: Simpath: an efficient algorithm for influence maximization under the linear threshold model. In: 2011 IEEE 11th International Conference on Data Mining (ICDM), pp. 211–220. IEEE (2011)
Jendoubi, S., Martin, A., Liétard, L., Hadji, H.B., Yaghlane, B.B.: Two evidential data based models for influence maximization in twitter. Knowl. Based Syst. 121, 58–70 (2017)
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)
Kitsak, M., et al.: Identification of influential spreaders in complex networks. Nat. Phys. 6(11), 888 (2010)
Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 420–429. ACM (2007)
Li, C., Ma, J., Guo, X., Mei, Q.: Deepcas: an end-to-end predictor of information cascades. In: Proceedings of the 26th International Conference on World Wide Web, pp. 577–586. International World Wide Web Conferences Steering Committee (2017)
Malliaros, F.D., Rossi, M.E.G., Vazirgiannis, M.: Locating influential nodes in complex networks. Sci. Rep. 6, 19,307 (2016)
Pei, S., Morone, F., Makse, H.A.: Theories for influencer identification in complex networks. Complex Spreading Phenomena in Social Systems, pp. 125–148. Springer, Berlin (2018)
Qiu, J., Tang, J., Ma, H., Dong, Y., Wang, K., Tang, J.: Deepinf: modeling influence locality in large social networks. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018) (2018)
Rodriguez, M.G., Balduzzi, D., Schölkopf, B.: Uncovering the temporal dynamics of diffusion networks. arXiv preprint arXiv:1105.0697 (2011)
Rodriguez, M.G., Schölkopf, B.: Influence maximization in continuous time diffusion networks. arXiv preprint arXiv:1205.1682 (2012)
Rossi, M.E.G., Vazirgiannis, M.: Exploring network centralities in spreading processes. In: International Symposium on Web Algorithms (ISWAG) (2016)
Saito, K., Kimura, M., Ohara, K., Motoda, H.: Learning continuous-time information diffusion model for social behavioral data analysis. In: Asian Conference on Machine Learning, pp. 322–337. Springer, Berlin (2009)
Saito, K., Nakano, R., Kimura, M.: Prediction of information diffusion probabilities for independent cascade model. In: International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, pp. 67–75. Springer, Berlin (2008)
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)
Tang, Y., Xiao, X., Shi, Y.: Influence maximization: near-optimal time complexity meets practical efficiency. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 75–86. ACM (2014)
Vespignani, A.: Modelling dynamical processes in complex socio-technical systems. Nat. Phys. 8(1), 32 (2012)
Xie, M., Yang, Q., Wang, Q., Cong, G., De Melo, G.: Dynadiffuse: a dynamic diffusion model for continuous time constrained influence maximization. In: AAAI, pp. 346–352 (2015)
Zhang, J., Liu, B., Tang, J., Chen, T., Li, J.: Social influence locality for modeling retweeting behaviors. In: IJCAI, vol. 13, pp. 2761–2767 (2013)
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Panagopoulos, G., Malliaros, F.D., Vazirgiannis, M. (2019). DiffuGreedy: An Influence Maximization Algorithm Based on Diffusion Cascades. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 812. Springer, Cham. https://doi.org/10.1007/978-3-030-05411-3_32
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