Improving Multi-objective Evolutionary Influence Maximization in Social Networks

Doina Bucur, Giovanni Iacca, Andrea Marcelli, Giovanni Squillero, Alberto Tonda*

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    4 Citations (Scopus)
    106 Downloads (Pure)

    Abstract

    In the context of social networks, maximizing influence means contacting the largest possible number of nodes starting from a set of seed nodes, and assuming a model for influence propagation. The real-world applications of influence maximization are of uttermost importance, and range from social studies to marketing campaigns. Building on a previous work on multi-objective evolutionary influence maximization, we propose improvements that not only speed up the optimization process considerably, but also deliver higher-quality results. State-of-the-art heuristics are run for different sizes of the seed sets, and the results are then used to initialize the population of a multi-objective evolutionary algorithm. The proposed approach is tested on three publicly available real-world networks, where we show that the evolutionary algorithm is able to improve upon the solutions found by the heuristics, while also converging faster than an evolutionary algorithm started from scratch.

    Original languageEnglish
    Title of host publicationApplications of Evolutionary Computation
    Subtitle of host publication21st International Conference, EvoApplications 2018, Proceedings
    EditorsKevin Sim, Paul Kaufmann
    PublisherSpringer
    Pages117-124
    Number of pages8
    ISBN (Electronic)978-3-319-77538-8
    ISBN (Print)978-3-319-77537-1
    DOIs
    Publication statusPublished - 1 Jan 2018
    EventEvoApplications. 21st International Conference on the Applications of Evolutionary Computation 2018 - Parma, Italy
    Duration: 4 Apr 20186 Apr 2018
    Conference number: 21
    http://www.evostar.org/2018/cfp_evoapps.php

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume10784 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    ConferenceEvoApplications. 21st International Conference on the Applications of Evolutionary Computation 2018
    Abbreviated titleEvostar 2018
    CountryItaly
    CityParma
    Period4/04/186/04/18
    Internet address

    Keywords

    • Influence maximization
    • Multi-objective evolutionary algorithms
    • Seeding
    • Social network

    Fingerprint Dive into the research topics of 'Improving Multi-objective Evolutionary Influence Maximization in Social Networks'. Together they form a unique fingerprint.

  • Cite this

    Bucur, D., Iacca, G., Marcelli, A., Squillero, G., & Tonda, A. (2018). Improving Multi-objective Evolutionary Influence Maximization in Social Networks. In K. Sim, & P. Kaufmann (Eds.), Applications of Evolutionary Computation: 21st International Conference, EvoApplications 2018, Proceedings (pp. 117-124). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10784 LNCS). Springer. https://doi.org/10.1007/978-3-319-77538-8_9