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Mitigating Misinformation Spreading in Social Networks via Edge Blocking

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PRICAI 2023: Trends in Artificial Intelligence (PRICAI 2023)

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Abstract

We study the problem of mitigating the spread of misinformation in social networks, simulated by the Independent Cascade model. We propose an intuitive community-based algorithm, which aims to detect well-connected communities in the network and disconnect the inter-community edges. Our experiments on real-world social networks demonstrate that the proposed algorithm significantly outperforms the prior methods, which mostly rely on centrality measures.

An extended version of this paper can be found in [23].

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Authors and Affiliations

  1. The Australian National University, Canberra, Australia

    Ahad N. Zehmakan

  2. Indian Institute of Technology (IIT) Delhi, New Delhi, India

    Khushvind Maurya

Authors
  1. Ahad N. Zehmakan
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  2. Khushvind Maurya
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Corresponding author

Correspondence to Khushvind Maurya .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Fenrong Liu

  2. SEEK Limited, Cremorne, NSW, Australia

    Arun Anand Sadanandan

  3. MIMOS (Malaysia), Kuala Lumpur, Malaysia

    Duc Nghia Pham

  4. Universitas Indonesia, Depok, Indonesia

    Petrus Mursanto

  5. Tabcorp Holdings Limited, Melbourne, VIC, Australia

    Dickson Lukose

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zehmakan, A.N., Maurya, K. (2024). Mitigating Misinformation Spreading in Social Networks via Edge Blocking. In: Liu, F., Sadanandan, A.A., Pham, D.N., Mursanto, P., Lukose, D. (eds) PRICAI 2023: Trends in Artificial Intelligence. PRICAI 2023. Lecture Notes in Computer Science(), vol 14325. Springer, Singapore. https://doi.org/10.1007/978-981-99-7019-3_10

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  • DOI: https://doi.org/10.1007/978-981-99-7019-3_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-7018-6

  • Online ISBN: 978-981-99-7019-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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