Advertisement

Journal of Combinatorial Optimization

, Volume 38, Issue 4, pp 1101–1127 | Cite as

Minimum budget for misinformation blocking in online social networks

  • Canh V. Pham
  • Quat V. Phu
  • Huan X. HoangEmail author
  • Jun Pei
  • My T. ThaiEmail author
Article
  • 78 Downloads

Abstract

Preventing misinformation spreading has recently become a critical topic due to an explosive growth of online social networks. Instead of focusing on blocking misinformation with a given budget as usually studied in the literatures, we aim to find the smallest set of nodes (minimize the budget) whose removal from a social network reduces the influence of misinformation (influence reduction) greater than a given threshold, called the Targeted Misinformation Blocking problem. We show that this problem is #P-hard under Linear Threshold and NP-hard under Independent Cascade diffusion models. We then propose several efficient algorithms, including approximation and heuristic algorithms to solve the problem. Experiments on real-world network topologies show the effectiveness and scalability of our algorithms that outperform other state-of-the-art methods.

Keywords

Misinformation Social network Approximation algorithm Optimization 

Notes

References

  1. Allcott H, Gentzkow M (2016) Social media and fake news in the 2016 election. Stanford Web https://web.stanford.edu/~gentzkow/research/fakenews.pdf. Accessed 21 July 2019
  2. Asahiro Y, Hassin R, Iwama K (2002) Complexity of finding dense subgraphs. Discrete Appl Math 121(1–3):15–26.  https://doi.org/10.1016/S0166-218X(01)00243-8 MathSciNetCrossRefzbMATHGoogle Scholar
  3. Budak C, Agrawal D, El Abbadi A (2011) Limiting the spread of misinformation in social networks. In: Proceedings of the 20th international conference on world wide web, WWW 2011, Hyderabad, India, March 28–April 1, 2011, pp 665–674.  https://doi.org/10.1145/1963405.1963499
  4. Chen W, Wang C, Wang Y (2010a) 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, Washington, DC, USA, July 25–28, 2010, pp 1029–1038.  https://doi.org/10.1145/1835804.1835934
  5. Chen W, Yuan Y, Zhang L (2010b) Scalable influence maximization in social networks under the linear threshold model. In: ICDM 2010, The 10th IEEE international conference on data mining, Sydney, Australia, 14–17 December 2010, pp 88–97.  https://doi.org/10.1109/ICDM.2010.118
  6. Cho E, Myers SA, Leskovec J (2011) Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, San Diego, CA, USA, August 21–24, 2011, pp 1082–1090.  https://doi.org/10.1145/2020408.2020579
  7. Domm P (2013) False rumor of explosion at white house causes stocks to briefly plunge; AP confirms its Twitter feed was hacked CNBC. CNBC http://www.cnbc.com/id/100646197. Accessed 21 July 2019
  8. Farajtabar M, Yang J, Ye X, Xu H, Trivedi R, Khalil EB, Li S, Song L, Zha H (2017) Fake news mitigation via point process based intervention. In: Proceedings of the 34th international conference on machine learning, ICML 2017, Sydney, NSW, Australia, 6–11 August 2017, pp 1097–1106. http://proceedings.mlr.press/v70/farajtabar17a.html
  9. Goyal A, Bonchi F, Lakshmanan LVS, Venkatasubramanian S (2013) On minimizing budget and time in influence propagation over social networks. Soc Netw Anal Min 3(2):179–192.  https://doi.org/10.1007/s13278-012-0062-z CrossRefGoogle Scholar
  10. He X, Song G, Chen W, Jiang Q (2012) Influence blocking maximization in social networks under the competitive linear threshold model. In: Proceedings of the Twelfth SIAM international conference on data mining, Anaheim, California, USA, April 26–28, 2012, pp 463–474.  https://doi.org/10.1137/1.9781611972825.40
  11. Kempe D, Kleinberg JM, Tardos É (2003) Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining, Washington, DC, USA, August 24–27, 2003, pp 137–146.  https://doi.org/10.1145/956750.956769
  12. Khalil EB, Dilkina BN, Song L (2014) Scalable diffusion-aware optimization of network topology. In: The 20th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’14, New York, NY, USA, August 24–27, 2014, pp 1226–1235.  https://doi.org/10.1145/2623330.2623704
  13. Kumar S, Spezzano F, Subrahmanian VS, Faloutsos C (2016) Edge weight prediction in weighted signed networks. In: IEEE 16th international conference on data mining, ICDM 2016, December 12–15, 2016, Barcelona, Spain, pp 221–230.  https://doi.org/10.1109/ICDM.2016.0033
  14. Leskovec J, Kleinberg JM, Faloutsos C (2005) Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proceedings of the eleventh ACM SIGKDD international conference on knowledge discovery and data mining, Chicago, Illinois, USA, August 21–24, 2005, pp 177–187.  https://doi.org/10.1145/1081870.1081893
  15. Leskovec J, Krause A, Guestrin C, Faloutsos C, VanBriesen JM, Glance NS (2007) Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, San Jose, California, USA, August 12–15, 2007, pp 420–429.  https://doi.org/10.1145/1281192.1281239
  16. Leskovec J, Lang KJ, Dasgupta A, Mahoney MW (2009) Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Internet Math 6(1):29–123.  https://doi.org/10.1080/15427951.2009.10129177 MathSciNetCrossRefzbMATHGoogle Scholar
  17. Nguyen HT, Cano A, Tam V, Dinh TN (2019) Blocking self-avoiding walks stops cyber-epidemics: a scalable GPU-based approach. IEEE Trans Knowl Data Eng.  https://doi.org/10.1109/TKDE.2019.2904969 CrossRefGoogle Scholar
  18. Nguyen NP, Yan G, Thai MT (2013) Analysis of misinformation containment in online social networks. Comput Netw 57(10):2133–2146.  https://doi.org/10.1016/j.comnet.2013.04.002 CrossRefGoogle Scholar
  19. Page L, Brin S, Motwani R, Winograd T (1998) The pagerank citation ranking: bringing order to the web. Technical Report, Stanford Digital Library Technologies ProjectGoogle Scholar
  20. Pham CV, Dinh HM, Nguyen HD, Dang HT, Hoang HX (2017) Limiting the spread of epidemics within time constraint on online social networks. In: Proceedings of the eighth international symposium on information and communication technology, Nha Trang City, Viet Nam, December 7–8, 2017, pp 262–269.  https://doi.org/10.1145/3155133.3155157
  21. Pham CV, Thai MT, Duong HV, Bui BQ, Hoang HX (2018) Maximizing misinformation restriction within time and budget constraints. J Comb Optim 35(4):1202–1240.  https://doi.org/10.1007/s10878-018-0252-3 MathSciNetCrossRefzbMATHGoogle Scholar
  22. Prakash BA, Tong H, Valler N, Faloutsos M, Faloutsos C (2010) Virus propagation on time-varying networks: theory and immunization algorithms. In: Machine learning and knowledge discovery in databases, European conference, ECML PKDD 2010, Barcelona, Spain, September 20–24, 2010, Proceedings, Part III, pp 99–114.  https://doi.org/10.1007/978-3-642-15939-8_7 CrossRefGoogle Scholar
  23. Ripeanu M, Foster IT, Iamnitchi A (2002) Mapping the gnutella network: properties of large-scale peer-to-peer systems and implications for system design. CoRR arXiv:cs.DC/0209028
  24. Song C, Hsu W, Lee M (2015) Node immunization over infectious period. In: Proceedings of the 24th ACM international conference on information and knowledge management, CIKM 2015, Melbourne, VIC, Australia, October 19–23, 2015, pp 831–840.  https://doi.org/10.1145/2806416.2806522
  25. Song C, Hsu W, Lee M (2017) Temporal influence blocking: minimizing the effect of misinformation in social networks. In: 33rd IEEE international conference on data engineering, ICDE 2017, San Diego, CA, USA, April 19–22, 2017, pp 847–858.  https://doi.org/10.1109/ICDE.2017.134
  26. Tong G, Wu W, Du D (2018a) Distributed rumor blocking with multiple positive cascades. IEEE Trans Comput Soc Syst 5(2):468–480.  https://doi.org/10.1109/TCSS.2018.2818661 CrossRefGoogle Scholar
  27. Tong GA, Wu W, Guo L, Li D, Liu C, Liu B, Du D (2017) An efficient randomized algorithm for rumor blocking in online social networks. In: 2017 IEEE conference on computer communications, INFOCOM 2017, Atlanta, GA, USA, May 1–4, 2017, pp 1–9.  https://doi.org/10.1109/INFOCOM.2017.8056957
  28. Tong GA, Du D, Wu W (2018b) On misinformation containment in online social networks. In: Advances in neural information processing systems 31: annual conference on neural information processing systems 2018, NeurIPS 2018, 3–8 December 2018, Montréal, Canada., pp 339–349. http://papers.nips.cc/paper/7317-on-misinformation-containment-in-online-social-networks
  29. Tong H, Prakash BA, Tsourakakis CE, Eliassi-Rad T, Faloutsos C, Chau DH (2010) On the vulnerability of large graphs. In: ICDM 2010, The 10th IEEE international conference on data mining, Sydney, Australia, 14–17 December 2010, pp 1091–1096.  https://doi.org/10.1109/ICDM.2010.54
  30. Valiant LG (1979) The complexity of enumeration and reliability problems. SIAM J Comput 8(3):410–421.  https://doi.org/10.1137/0208032 MathSciNetCrossRefzbMATHGoogle Scholar
  31. Zhang Y, Prakash BA (2015) Data-aware vaccine allocation over large networks. TKDD 10(2):20:1–20:32.  https://doi.org/10.1145/2803176 CrossRefGoogle Scholar
  32. Zhang H, Alim MA, Li X, Thai MT, Nguyen HT (2016a) Misinformation in online social networks: detect them all with a limited budget. ACM Trans Inf Syst 34(3):18:1–18:24.  https://doi.org/10.1145/2885494 CrossRefGoogle Scholar
  33. Zhang Y, Adiga A, Saha S, Vullikanti A, Prakash BA (2016b) Near-optimal algorithms for controlling propagation at group scale on networks. IEEE Trans Knowl Data Eng 28(12):3339–3352.  https://doi.org/10.1109/TKDE.2016.2605088 CrossRefGoogle Scholar
  34. Zhang H, Nguyen DT, Zhang H, Thai MT (2016c) Least cost influence maximization across multiple social networks. IEEE/ACM Trans Netw (TON) 24(2):929–939CrossRefGoogle Scholar
  35. Zheng J, Pan L (2018) Least cost rumor community blocking optimization in social networks. In: 2018 third international conference on security of smart cities, industrial control system and communications (SSIC), pp 1–5.  https://doi.org/10.1109/SSIC.2018.8556739
  36. Zhu W, Yang W, Xuan S, Man D, Wang W, Du X, Guizani M (2019) Location-based seeds selection for influence blocking maximization in social networks. IEEE Access 7:27272–27287.  https://doi.org/10.1109/ACCESS.2019.2900708 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of Engineering and Technology, Vietnam National UniversityHanoiVietnam
  2. 2.Department of Computer and Information Science and EngineeringUniversity of FloridaGainesvilleUSA
  3. 3.People’s Security AcademyHanoiVietnam
  4. 4.School of ManagementHefei University of TechnologyHefeiChina

Personalised recommendations