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A unifying framework for fairness-aware influence maximization

Golnoosh Farnadi, Behrouz Babaki et Michel Gendreau

Communication écrite (2020)

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Abstract

The problem of selecting a subset of nodes with greatest influence in a graph, commonly known as influence maximization, has been well studied over the past decade. This problem has real world applications which can potentially affect lives of individuals. Algorithmic decision making in such domains raises concerns about their societal implications. One of these concerns, which surprisingly has only received limited attention so far, is algorithmic bias and fairness. We propose a flexible framework that extends and unifies the existing works in fairness-aware influence maximization. This framework is based on an integer programming formulation of the influence maximization problem. The fairness requirements are enforced by adding linear constraints or modifying the objective function. Contrary to the previous work which designs specific algorithms for each variant, we develop a formalism which is general enough for specifying different notions of fairness. A problem defined in this formalism can be then solved using efficient mixed integer programming solvers. The experimental evaluation indicates that our framework not only is general but also is competitive with existing algorithms.

Mots clés

Group Fairness; Influence Maximization; Mixed Integer Programming

Sujet(s): 2700 Technologie de l'information > 2700 Technologie de l'information
2700 Technologie de l'information > 2713 Algorithmes
2700 Technologie de l'information > 2717 Études de modélisation et de simulation
Département: Département de génie informatique et génie logiciel
Centre de recherche: Autre
Organismes subventionnaires: Canada First Research Excellence Fund (CFREF), Calcul Québec, Compute Canada
URL de PolyPublie: https://publications.polymtl.ca/9245/
Nom de la conférence: WWW '20: The Web Conference 2020
Lieu de la conférence: Taipei, Taiwan
Date(s) de la conférence: 2020-04-20 - 2020-04-24
Maison d'édition: Association for Computing Machinery
DOI: 10.1145/3366424.3383555
URL officielle: https://doi.org/10.1145/3366424.3383555
Date du dépôt: 22 nov. 2021 14:00
Dernière modification: 24 oct. 2023 12:48
Citer en APA 7: Farnadi, G., Babaki, B., & Gendreau, M. (avril 2020). A unifying framework for fairness-aware influence maximization [Communication écrite]. WWW '20: The Web Conference 2020, Taipei, Taiwan. https://doi.org/10.1145/3366424.3383555

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