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Distance Vector Domination

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SOFSEM 2025: Theory and Practice of Computer Science (SOFSEM 2025)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15538))

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Abstract

Identifying and mitigating the spread of fake information is a challenging issue that has become dominant with the rise of social media. We consider a generalization of the Domination problem that can be used to detect a set of individuals who, once immunized, can prevent the spreading of fake narratives. The considered problem, named Distance Vector Domination generalizes both distance and multiple domination, at individual (i.e., vertex) level. We study the parameterized complexity of the problem according to several standard and structural parameters. We prove the W[1]-hardness of the problem with respect to neighborhood diversity, even when all the distances are 1. We also give fixed-parameter algorithms for some variants of the problem and parameter combinations.

Work partially supported by project SERICS (PE00000014) under the MUR National Recovery and Resilience Plan funded by the European Union - NextGenerationEU.

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Notes

  1. 1.

    For a positive integer a, we use [a] to denote the set of integers \([a] = \{1, 2, \ldots , a\}\).

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Author information

Authors and Affiliations

  1. Department of Psychology, University of Campania “L.Vanvitelli”, Caserta, Italy

    Gennaro Cordasco

  2. Department of Computer Science, University of Salerno, Fisciano, Italy

    Luisa Gargano & Adele A. Rescigno

Authors
  1. Gennaro Cordasco
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  2. Luisa Gargano
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  3. Adele A. Rescigno
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Correspondence to Gennaro Cordasco .

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Editors and Affiliations

  1. Comenius University in Bratislava, Bratislava, Slovakia

    Rastislav Královič

  2. Czech Academy of Sciences, Prague, Czech Republic

    Věra Kůrková

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Cordasco, G., Gargano, L., Rescigno, A.A. (2025). Distance Vector Domination. In: Královič, R., Kůrková, V. (eds) SOFSEM 2025: Theory and Practice of Computer Science. SOFSEM 2025. Lecture Notes in Computer Science, vol 15538. Springer, Cham. https://doi.org/10.1007/978-3-031-82670-2_14

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  • DOI: https://doi.org/10.1007/978-3-031-82670-2_14

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