Abstract
Influence maximization in the continuous-time domain is a prevalent topic in social media analytics. It relates to the problem of identifying those individuals in a social network, whose endorsement of an opinion will maximize the number of expected follow-ups within a finite time window. This work presents a novel GPU-accelerated algorithm that enables node-parallel estimation of influence spread in the continuous-time domain. Given a finite time window, the method involves decomposing a social graph into multiple local regions within which influence spread can be estimated in parallel to allow for fast and low-cost computations. Experiments show that the proposed method achieves up to x85 speed-up vs. the state-of-the-art on real-world social graphs with up to 100K nodes and 2.5M edges. In addition, our optimization solutions are within 98.9% of the influence spread achieved by current state-of-the-art. The memory consumption of our method is also substantially lower. Indicatively, our method can achieve, on a single GPU, similar running time performance as the state-of-the-art, when the latter distributes execution across hundreds of CPU cores.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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, KDD’10, pp. 1029–1038 (2010)
Cohen, E.: Size-estimation framework with applications to transitive closure and reachability. J. Comput. Syst. Sci. 55(3), 441–453 (1997)
Du, N., Song, L., Gomez-Rodriguez, M., Zha, H.: Scalable influence estimation in continuous-time diffusion networks. In: Advances in Neural Information Processing Systems, NIPS’13 (2013)
Du, N., Song, L., Yuan, M., Smola, A.J.: Learning networks of heterogeneous influence. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp. 2780–2788 (2012)
Gomez-Rodriguez, M., Balduzzi, D., Schölkopf, B.: Uncovering the temporal dynamics of diffusion networks. In: Proceedings of the 28th International Conference on Machine Learning, pp. 561–568 (2011). http://www.icml-2011.org/papers/354_icmlpaper.pdf
Gomez-Rodriguez, M., Schölkopf, B.: Influence maximization in continuous time diffusion networks. In: Proceedings of the 29th International Conference on Machine Learning, pp. 313–320 (2012)
Goyal, A., Lu, W., Lakshmanan, L.V.S.: Simpath: an efficient algorithm for influence maximization under the linear threshold model. In: 2011 IEEE 11th International Conference on Data Mining, pp. 211–220 (2011)
Horel, T., Singer, Y.: Maximization of approximately submodular functions. In: Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29, pp. 3045–3053. Curran Associates, Inc. (2016)
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’03, pp. 137–146 (2003)
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, KDD’07, pp. 420–429 (2007)
Leskovec, J., Krevl, A.: SNAP Datasets: Stanford large network dataset collection, June 2014. http://snap.stanford.edu/data
Li, L., Zha, H.: Learning parametric models for social infectivity in multi-dimensional hawkes processes. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence, AAAI’14, pp. 101–107 (2014)
Liu, X., Li, M., Li, S., Peng, S., Liao, X., Lu, X.: Imgpu: Gpu-accelerated influence maximization in large-scale social networks. IEEE Transactions on Parallel and Distributed Systems 25(1), 136–145 (2014)
Nemhauser, G.L., Wolsey, L.A., Fisher, M.L.: An analysis of approximations for maximizing submodular set functions. Mathematical Programming 14(1), 265–294 (1978)
Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’02, pp. 61–70 (2002)
Yang, S.H., Zha, H.: Mixture of mutually exciting processes for viral diffusion. In: Proceedings of the 30th International Conference on Machine Learning, Vol. 28, pp. 1–9 (2013)
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
Pal, K., Poulos, Z., Kim, E., Veneris, A. (2017). Fast GPU-Based Influence Maximization Within Finite Deadlines via Node-Level Parallelism. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2017. Lecture Notes in Computer Science(), vol 10357. Springer, Cham. https://doi.org/10.1007/978-3-319-62701-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-62701-4_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-62700-7
Online ISBN: 978-3-319-62701-4
eBook Packages: Computer ScienceComputer Science (R0)