Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/105713
Title: Revisiting the stop-and-stare algorithms for influence maximization
Authors: Huang, Keke
Wang, Sibo
Bevilacqua, Glenn
Xiao, Xiaokui
Lakshmanan, Laks V. S.
Keywords: Social Network
Engineering::Computer science and engineering
SSA-Fix
Issue Date: 2017
Source: Huang, K., Wang, S., Bevilacqua, G., Xiao, X., & Lakshmanan, L. V. S. (2017). Revisiting the stop-and-stare algorithms for influence maximization. Proceedings of the VLDB Endowment, 10(9), 913-924. doi:10.14778/3099622.3099623
Series/Report no.: Proceedings of the VLDB Endowment
Abstract: Influence maximization is a combinatorial optimization problem that finds important applications in viral marketing, feed recommendation, etc. Recent research has led to a number of scalable approximation algorithms for influence maximization, such as TIM+ and IMM, and more recently, SSA and D-SSA. The goal of this paper is to conduct a rigorous theoretical and experimental analysis of SSA and D-SSA and compare them against the preceding algorithms. In doing so, we uncover inaccuracies in previously reported technical results on the accuracy and efficiency of SSA and D-SSA, which we set right. We also attempt to reproduce the original experiments on SSA and D-SSA, based on which we provide interesting empirical insights. Our evaluation confirms some results reported from the original experiments, but it also reveals anomalies in some other results and sheds light on the behavior of SSA and D-SSA in some important settings not considered previously. We also report on the performance of SSA-Fix, our modification to SSA in order to restore the approximation guarantee that was claimed for but not enjoyed by SSA. Overall, our study suggests that there exist opportunities for further scaling up influence maximization with approximation guarantees.
URI: https://hdl.handle.net/10356/105713
http://hdl.handle.net/10220/49548
ISSN: 2150-8097
DOI: 10.14778/3099622.3099623
Rights: © 2017 VLDB Endowment. This work is licensed under the Creative Commons AttributionNonCommercial-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. For any use beyond those covered by this license, obtain permission by emailing info@vldb.org.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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