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VAIM: Visual Analytics for Influence Maximization

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Graph Drawing and Network Visualization (GD 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12590))

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Abstract

In social networks, individuals’ decisions are strongly influenced by recommendations from their friends and acquaintances. The influence maximization (IM) problem asks to select a seed set of users that maximizes the influence spread, i.e., the expected number of users influenced through a stochastic diffusion process triggered by the seeds. In this paper, we present VAIM, a visual analytics system that supports users in analyzing the information diffusion process determined by different IM algorithms. By using VAIM one can: (i) simulate the information spread for a given seed set on a large network, (ii) analyze and compare the effectiveness of different seed sets, and (iii) modify the seed sets to improve the corresponding influence spread.

Research of WD, GL and FM partially supported by: (i) MIUR, grant 20174LF3T8 “AHeAD: efficient Algorithms for HArnessing networked Data”, (ii) Dip. di Ingegneria - Università degli Studi di Perugia, grant RICBA19FM: “Modelli, algoritmi e sistemi per la visualizzazione di grafi e reti”. Research of AA and SM partially supported by TU Wien “Smart CT” research cluster.

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References

  1. Arleo, A., Didimo, W., Liotta, G., Miksch, S., Montecchiani, F.: Vaim: Visual analytics for influence maximization. arXiv:2008.08821v1 [cs.SI] (2020). https://arxiv.org/abs/2008.08821

  2. Arleo, A., Didimo, W., Liotta, G., Montecchiani, F.: Large graph visualizations using a distributed computing platform. Inf. Sci. 381, 124–141 (2017). https://doi.org/10.1016/j.ins.2016.11.012

    Article  Google Scholar 

  3. Arleo, A., Didimo, W., Liotta, G., Montecchiani, F.: A distributed multilevel force-directed algorithm. IEEE Trans. Parallel Distrib. Syst. 30(4), 754–765 (2019). https://doi.org/10.1109/TPDS.2018.2869805

    Article  MATH  Google Scholar 

  4. Arora, A., Galhotra, S., Ranu, S.: Debunking the myths of influence maximization: an in-depth benchmarking study. In: SIGMOD Conference, pp. 651–666. ACM (2017)

    Google Scholar 

  5. Cao, N., Lin, Y., Sun, X., Lazer, D., Liu, S., Qu, H.: Whisper: tracing the spatiotemporal process of information diffusion in real time. IEEE Trans. Vis. Comput. Graph. 18(12), 2649–2658 (2012)

    Article  Google Scholar 

  6. Chen, S., et al.: D-Map: visual analysis of ego-centric information diffusion patterns in social media. In: VAST, pp. 41–50. IEEE Computer Society (2016)

    Google Scholar 

  7. Chen, S., Lin, L., Yuan, X.: Social media visual analytics. Comput. Graph. Forum 36(3), 563–587 (2017)

    Article  Google Scholar 

  8. Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: KDD, pp. 1029–1038. ACM (2010)

    Google Scholar 

  9. Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, pp. 199–208. Association for Computing Machinery, New York (2009). https://doi.org/10.1145/1557019.1557047

  10. Guille, A., Hacid, H., Favre, C., Zighed, D.A.: Information diffusion in online social networks: a survey. SIGMOD Rec. 42(2), 17–28 (2013)

    Article  Google Scholar 

  11. Harrower, M., Brewer, C.A.: Colorbrewer.org: an online tool for selecting colour schemes for maps. Cartographic J. 40(1), 27–37 (2003)

    Article  Google Scholar 

  12. Kempe, D., Kleinberg, J.M., Tardos, É.: Maximizing the spread of influence through a social network. In: KDD, pp. 137–146. ACM (2003)

    Google Scholar 

  13. Kempe, D., Kleinberg, J.M., Tardos, É.: Maximizing the spread of influence through a social network. Theory Comput. 11, 105–147 (2015). https://doi.org/10.4086/toc.2015.v011a004

    Article  MathSciNet  MATH  Google Scholar 

  14. Kobourov, S.G.: Force-directed drawing algorithms. In: Tamassia, R. (ed.) Handbook on Graph Drawing and Visualization, pp. 383–408. Chapman and Hall/CRC, Boca Raton (2013)

    Google Scholar 

  15. Leskovec, J., Mcauley, J.J.: Learning to discover social circles in ego networks. In: Advances in Neural Information Processing Systems, pp. 539–547 (2012)

    Google Scholar 

  16. Li, Y., Fan, J., Wang, Y., Tan, K.: Influence maximization on social graphs: a survey. IEEE Trans. Knowl. Data Eng. 30(10), 1852–1872 (2018)

    Article  Google Scholar 

  17. Long, C., Wong, R.C.: Visual-VM: a social network visualization tool for viral marketing. In: ICDM Workshops, pp. 1223–1226. IEEE Computer Society (2014)

    Google Scholar 

  18. Marcus, A., Bernstein, M.S., Badar, O., Karger, D.R., Madden, S., Miller, R.C.: Processing and visualizing the data in tweets. SIGMOD Rec. 40(4), 21–27 (2011)

    Article  Google Scholar 

  19. Marcus, A., Bernstein, M.S., Badar, O., Karger, D.R., Madden, S., Miller, R.C.: TwitInfo: aggregating and visualizing microblogs for event exploration. In: CHI, pp. 227–236. ACM (2011)

    Google Scholar 

  20. Miksch, S., Aigner, W.: A matter of time: applying a data-users-tasks design triangle to visual analytics of time-oriented data. Comput. Graph. 38, 286–290 (2014)

    Article  Google Scholar 

  21. Sun, G., Tang, T., Peng, T., Liang, R., Wu, Y.: Socialwave: visual analysis of spatio-temporal diffusion of information on social media. ACM TIST 9(2), 15:1–15:23 (2018)

    Google Scholar 

  22. Vallet, J., Kirchner, H., Pinaud, B., Melançon, G.: A visual analytics approach to compare propagation models in social networks. In: Rensink, A., Zambon, E. (eds.) Proceedings Graphs as Models, GaM@ETAPS 2015, London, UK, 11–12 April 2015. EPTCS, vol. 181, pp. 65–79 (2015). https://doi.org/10.4204/EPTCS.181.5

  23. Vallet, J., Pinaud, B., Melançon, G.: Studying propagation dynamics in networks through rule-based modeling. In: Chen, M., Ebert, D.S., North, C. (eds.) 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014, Paris, France, 25–31 October 2014, pp. 281–282. IEEE Computer Society (2014). https://doi.org/10.1109/VAST.2014.7042530

  24. Wu, Y., Liu, S., Yan, K., Liu, M., Wu, F.: OpinionFlow: visual analysis of opinion diffusion on social media. IEEE Trans. Vis. Comput. Graph. 20(12), 1763–1772 (2014)

    Article  Google Scholar 

  25. Zhao, J., Cao, N., Wen, Z., Song, Y., Lin, Y., Collins, C.: #FluxFlow: visual analysis of anomalous information spreading on social media. IEEE Trans. Vis. Comput. Graph. 20(12), 1773–1782 (2014)

    Article  Google Scholar 

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Authors and Affiliations

  1. TU Wien, Vienna, Austria

    Alessio Arleo & Silvia Miksch

  2. Università degli Studi di Perugia, Perugia, Italy

    Walter Didimo, Giuseppe Liotta & Fabrizio Montecchiani

Authors
  1. Alessio Arleo
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  2. Walter Didimo
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  3. Giuseppe Liotta
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  4. Silvia Miksch
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  5. Fabrizio Montecchiani
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Corresponding author

Correspondence to Alessio Arleo .

Editor information

Editors and Affiliations

  1. LaBRI, University of Bordeaux, Talence, France

    David Auber

  2. Charles University, Prague, Czech Republic

    Pavel Valtr

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Arleo, A., Didimo, W., Liotta, G., Miksch, S., Montecchiani, F. (2020). VAIM: Visual Analytics for Influence Maximization. In: Auber, D., Valtr, P. (eds) Graph Drawing and Network Visualization. GD 2020. Lecture Notes in Computer Science(), vol 12590. Springer, Cham. https://doi.org/10.1007/978-3-030-68766-3_9

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  • DOI: https://doi.org/10.1007/978-3-030-68766-3_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-68765-6

  • Online ISBN: 978-3-030-68766-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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