Abstract
SSA/DSSA were introduced in SIGMOD’16 as the first algorithms that can provide rigorous \(1-1/e-\epsilon \) guarantee with fewer samples than the worst-case sample complexity \(O(nk \frac{\log n}{\epsilon ^2 OPT_k})\). They are order of magnitude faster than the existing methods. The original SIGMOD’16 paper, however, contains errors, and the new fixes for SSA/DSSA, referred to as SSA-fix and D-SSA-fix, have been published in the extended version of the paper [11]. In this paper, we affirm the correctness on accuracy and efficiency of SSA-fix/D-SSA-fix algorithms. Specifically, we refuse the misclaims on ‘important gaps’ in the proof of D-SSA-fix’s efficiency raised by Huang et al. [5] published in VLDB in May 2017. We also replicate the experiments to dispute the experimental discrepancies shown in [5]. Our experiment results indicate that implementation/modification details and data pre-processing attribute for most discrepancies in running-time. (We requested the modified code from VLDB’17 [5] last year but have not received the code from the authors. We also sent them the explanation for the gaps they misclaimed for the D-SSA-fix’s efficiency proof but have not received their concrete feedback.)
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References
Borgs, C., Brautbar, M., Chayes, J., Lucier, B.: Maximizing social influence in nearly optimal time. In: Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 946–957. SIAM (2014)
Chen, W.: An issue in the martingale analysis of the influence maximization algorithm IMM. arXiv preprint arXiv:1808.09363 (2018)
Chen, W., Lakshmanan, L.V., Castillo, C.: Information and influence propagation in social networks. Synth. Lect. Data Manag. 5(4), 1–177 (2013)
Huang, K., Wang, S., Bevilacqua, G., Xiao, X., Lakshmanan, L.V.S.: Revisiting the Stop-and-Stare Algorithms for Influence Maximization. https://sites.google.com/site/vldb2017imexptr/
Huang, K., Wang, S., Bevilacqua, G., Xiao, X., Lakshmanan, L.V.S.: Revisiting the stop-and-stare algorithms for influence maximization. Proc. VLDB Endow. 10(9), 913–924 (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)
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: WWW, pp. 591–600. ACM (2010)
Nguyen, H.T., Thai, M.T., Dinh, T.N.: Stop-and-stare: optimal sampling algorithms for viral marketing in billion-scale networks. In: Proceedings of the 2016 International Conference on Management of Data, pp. 695–710. ACM (2016)
Nguyen, H.T., Thai, M.T., Dinh, T.N.: Stop-and-stare: optimal sampling algorithms for viral marketing in billion-scale networks. arXiv preprint arXiv:1605.07990v2 (2016). Accessed 7 Sep 2016
Nguyen, H.T., Thai, M.T., Dinh, T.N.: A billion-scale approximation algorithm for maximizing benefit in viral marketing. IEEE/ACM Trans. Netw. (TON) 25(4), 2419–2429 (2017)
Nguyen, H.T., Thai, M.T., Dinh, T.N.: Stop-and-stare: optimal sampling algorithms for viral marketing in billion-scale networks. arXiv preprint arXiv:1605.07990 (2017). Accessed 22 Feb 2017
Nguyen, H.T., Thai, M.T., Dinh, T.N.: SSA/DSSA Implementations. https://github.com/hungnt55/Stop-and-Stare (2018). Accessed 16 May 2018
Nguyen, H.T., Dinh, T.N., Thai, M.T.: Cost-aware targeted viral marketing in billion-scale networks. In: INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, pp. 1–9. IEEE (2016)
Nguyen, H.T., Nguyen, T.P., Phan, N., Dinh, T.N.: Importance sketching of influence dynamics in billion-scale networks. arXiv preprint arXiv:1709.03565 (2017)
Tang, J., Tang, X., Yuan, J.: Influence maximization meets efficiency and effectiveness: a hop-based approach. In: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, ASONAM 2017, pp. 64–71. ACM, New York (2017). http://doi.acm.org/10.1145/3110025.3110041
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, SIGMOD 2015, pp. 1539–1554. ACM, New York (2015). http://doi.acm.org/10.1145/2723372.2723734
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)
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Nguyen, H.T., Dinh, T.N., Thai, M.T. (2018). Revisiting of ‘Revisiting the Stop-and-Stare Algorithms for Influence Maximization’. In: Chen, X., Sen, A., Li, W., Thai, M. (eds) Computational Data and Social Networks. CSoNet 2018. Lecture Notes in Computer Science(), vol 11280. Springer, Cham. https://doi.org/10.1007/978-3-030-04648-4_23
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