Skip to main content
SpringerLink
Log in
Book cover

International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation

SBP-BRiMS 2020: Social, Cultural, and Behavioral Modeling pp 192–201Cite as

Developing Graph Theoretic Techniques to Identify Amplification and Coordination Activities of Influential Sets of Users

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12268))

Abstract

Social media, with its accessibility and anonymity, has helped malicious actors to thrive and coordinate several campaigns. Such users successfully utilize social media to coordinate different kinds of movements that could influence political aspects, damage the crucial infrastructure and affect the economy of several countries around the world. Malicious users could coordinate to cripple the transportation system by closing the main highways and bridges in big cities or spreading false security information that causes panic and hysteria in large societies. Since the traditional community detection methods fall short in finding these users, our research proposes an integrated model to find, analyze, and suspend these coordinated malicious sets of users in online complex networks. The Focal Structures Analysis model is a two-level analysis to study individual-level features using closeness centrality and group-level features by implementing the spectral modularity method. The model decomposes the interactions between both individual-level and group-level to find key sets of users that are responsible for propagating behavior through online social media platforms. The proposed model is applied to a fake news YouTube co-commenter network. The outcomes were validated via modularity methods and depth-first search to measure each set’s influence at individual-level and at the entire network-level.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Şen, F., Wigand, R., Agarwal, N., Tokdemir, S., Kasprzyk, R.: Focal structures analysis: identifying influential sets of individuals in a social network. Soc. Netw. Anal. Min. 6(1), 17 (2016)

    Article  Google Scholar 

  2. Hussain, M.N., Tokdemir, S., Agarwal, N. , Al-Khateeb, S.: Analyzing disinformation and crowd manipulation tactics on YouTube. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1092–1095 (2018)

    Google Scholar 

  3. Zafarani, R., Abbasi, M.A., Liu, H.: Social Media Mining: An Introduction. Cambridge University Press, Cambridge (2014)

    Book  Google Scholar 

  4. Girvan, M., Newman, M.: Community structure in social and biological networks. PNAS 99(12), 7821–7826 (2002)

    Article  MathSciNet  Google Scholar 

  5. Yazdanparast, S., Havens, T.C.: Modularity maximization using completely positive programming. Phys. A Stat. Mech. Appl. 471, 20–32 (2017)

    Article  MathSciNet  Google Scholar 

  6. Tsung, C.K., Ho, H., Chou, S., Lin, J., Lee, S.: A spectral clustering approach based on modularity maximization for community detection problem. In: Proceedings of the International Computer Symposium, ICS 2016, pp. 12–17 (2017)

    Google Scholar 

  7. Leskovec, J., McGlohon, M., Faloutsos, C., Glance, N., Hurst, M.: Cascading behavior in large blog graphs. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 551–556 (2007)

    Google Scholar 

  8. Li, C., Wang, L., Sun, S., Xia, C.: Identification of influential spreaders based on classified neighbors in real-world complex networks. Appl. Math. Comput. 320(11), 512–523 (2018)

    Article  MathSciNet  Google Scholar 

  9. Borgatti, S.P.: Centrality and network flow. Soc. Netw. 27(1), 55–71 (2005)

    Article  MathSciNet  Google Scholar 

  10. Agarwal, N., Liu, H., Tang, L., Yu, P.S.: Modeling blogger influence in a community. Soc. Netw. Anal. Min. 2(2), 139–162 (2012)

    Article  Google Scholar 

  11. Agarwal, N., Liu, H., Tang, L., Yu, P.S.: Identifying the influential bloggers in a community. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 207–218 (2008)

    Google Scholar 

  12. Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: Proceedings of Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 61–70 (2002)

    Google Scholar 

  13. Kempe, D., Kleinberg, J.: Maximizing the spread of influence through a social network. In: Proceedings of Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146 (2003)

    Google Scholar 

  14. Chen, W., Wang, Y.: Efficient influence maximization in social networks categories and subject descriptors. In: Proceedings of 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 199–207 (2009)

    Google Scholar 

  15. Leskovec, J., McGlohon, M., Faloutsos, C., Glance, N., Hurst, M.: Patterns of cascading behavior in large blog graphs. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 551–556 (2007)

    Google Scholar 

  16. Kivran-Swaine, F., Govindan, P., Naaman, M.: The impact of network structure on breaking ties in online social networks. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1101–1104 (2011)

    Google Scholar 

  17. Chua, T.-S.: The Multimedia Challenges in Social Media Analytics. In: Proceedings of the 3rd International Workshop on Socially-Aware Multimedia, pp. 17–18 (2014)

    Google Scholar 

  18. Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. World Wide Web Internet Web Inf. Syst. 54 (1999–66) 1–17 (1998)

    Google Scholar 

  19. Kleinberg, J.O.N.M.: Authoritative sources in a hyperlinked environment. In: Proceedings of the ACM-SIAM Symposium on Discrete Algorithms, vol. 46, no. 5, pp. 604–632 (1999)

    Google Scholar 

  20. Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 61–70 (2002)

    Google Scholar 

  21. Von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2007)

    Article  MathSciNet  Google Scholar 

  22. Hagen, L., Member, S., Kahng, A.B.: New spectral methods for ratio cut partitioning and clustering. IEEE Trans. Comput. Des. Integr. Circ. Syst. 11(9), 1074–1085 (1992)

    Article  Google Scholar 

  23. Blondel, V.D., Guillaume, J., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 10, 10008 (2008)

    Article  Google Scholar 

  24. Sato, K., Izunaga, Y.: An enhanced MILP-based branch-and-price approach to modularity density maximization on graphs. Comput. Oper. Res. 106, 236–245 (2018)

    Article  MathSciNet  Google Scholar 

  25. Newman, M.E.J.: Detecting community structure in networks. Eur. Phys. J. B 38(2), 321–330 (2004). https://doi.org/10.1140/epjb/e2004-00124-y

    Article  Google Scholar 

  26. Java, A., Joshi, A., Finin, T.: Detecting communities via simultaneous clustering of graphs and folksonomies. In: Proceedings of Tenth Workshop Web Mining. and Web usage Analysis (2008)

    Google Scholar 

  27. Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)

    Article  Google Scholar 

  28. Wang, G., Shen, Y., Luan, E.: Measure of centrality based on modularity matrix. Prog. Nat. Sci. 18(8), 1043–1047 (2008)

    Article  Google Scholar 

  29. Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks, pp. 1–16 (2003)

    Google Scholar 

  30. Søe, S.O.: Algorithmic detection of misinformation and disinformation: Gricean perspectives. J. Doc. 74(2), 309–332 (2018)

    Article  Google Scholar 

  31. Zhang, X., Ghorbani, A.A.: An overview of online fake news: characterization, detection, and discussion. Inf. Process. Manag. 57, 102025 (2019)

    Article  Google Scholar 

  32. Shao, C., Ciampaglia, G.L., Flammini, A., Menczer, F.: Hoaxy: a platform for tracking online misinformation, pp. 745–750 (2016)

    Google Scholar 

  33. Shu, K., Sliva, A., Wang, S., Tand, J., Liu, H.: Fake news detection: network data from social media used to predict fakes. ACM SIGKDD Explor. Newsl. 19(1), 22–36 (2017)

    Article  Google Scholar 

  34. Alassad, M., Agarwal, N., Hussain, M.N.: Examining intensive groups in YouTube commenter networks. In: Proceedings of the 12th International Conference on SBP-BRiMS 2019, no. 12, pp. 224–233 (2019)

    Google Scholar 

  35. Alassad, M., Hussain, M.N., Agarwal, N.: Finding fake news key spreaders in complex social networks by using bi-level decomposition optimization method. In: Agarwal, N., Sakalauskas, L., Weber, G.-W. (eds.) MSBC 2019. CCIS, vol. 1079, pp. 41–54. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29862-3_4

    Chapter  Google Scholar 

  36. Tarjan, R.: Depth-first search and linear graph algorithms. SIAM J. Comput. 1(2), 146–160 (1972)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgment

This research is funded in part by the U.S. National Science Foundation (OIA-1946391, OIA-1920920, IIS-1636933, ACI-1429160, and IIS-1110868), U.S. Office of Naval Research (N00014-10-1-0091, N00014-14-1-0489, N00014-15-P-1187, N00014-16-1-2016, N00014-16-1-2412, N00014-17-1-2675, N00014-17-1-2605, N68335-19-C-0359, N00014-19-1-2336, N68335-20-C-0540), U.S. Air Force Research Lab, U.S. Army Research Office (W911NF-17-S-0002, W911NF-16-1-0189), U.S. Defense Advanced Research Projects Agency (W31P4Q-17-C-0059), Arkansas Research Alliance, the Jerry L. Maulden/Entergy Endowment at the University of Arkansas at Little Rock, and the Australian Department of Defense Strategic Policy Grants Program (SPGP) (award number: 2020-106-094). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding organizations. The researchers gratefully acknowledge the support.

Author information

Authors and Affiliations

  1. University of Arkansas, Little Rock, AR, USA

    Mustafa Alassad, Muhammad Nihal Hussain & Nitin Agarwal

Authors
  1. Mustafa Alassad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhammad Nihal Hussain
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nitin Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mustafa Alassad .

Editor information

Editors and Affiliations

  1. United States Military Academy, West Point, NY, USA

    Robert Thomson

  2. University of Michigan-Flint, Flint, MI, USA

    Halil Bisgin

  3. Bucknell University, Lewisburg, PA, USA

    Christopher Dancy

  4. The Ohio State University, Columbus, OH, USA

    Ayaz Hyder

  5. University of Arkansas at Little Rock, Little Rock, AR, USA

    Muhammad Hussain

Appendix I: Zachary Karate Club Network [6]

Appendix I: Zachary Karate Club Network [6]

figure a

A) Karate club network clustered by modularity method into 4 communities.

B) Results presented from the model proposed by Şen et al. [1].

(F1) – (F11) Focal structure sets identified by the proposed method, overcome the state of the art drawbacks.

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Alassad, M., Hussain, M.N., Agarwal, N. (2020). Developing Graph Theoretic Techniques to Identify Amplification and Coordination Activities of Influential Sets of Users. In: Thomson, R., Bisgin, H., Dancy, C., Hyder, A., Hussain, M. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2020. Lecture Notes in Computer Science(), vol 12268. Springer, Cham. https://doi.org/10.1007/978-3-030-61255-9_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-61255-9_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61254-2

  • Online ISBN: 978-3-030-61255-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation