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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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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

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