Identifying and Eliminating Majority Illusion in Social Networks

Authors

  • Umberto Grandi University of Toulouse, France
  • Lawqueen Kanesh IIT Jodhpur, India
  • Grzegorz Lisowski University of Warwick, UK
  • Ramanujan Sridharan University of Warwick, UK
  • Paolo Turrini University of Warwick, UK

DOI:

https://doi.org/10.1609/aaai.v37i4.25634

Keywords:

APP: Social Networks, GTEP: Social Choice / Voting, KRR: Computational Complexity of Reasoning

Abstract

Majority illusion occurs in a social network when the majority of the network vertices belong to a certain type but the majority of each vertex's neighbours belong to a different type, therefore creating the wrong perception, i.e., the illusion, that the majority type is different from the actual one. From a system engineering point of view, this motivates the search for algorithms to detect and, where possible, correct this undesirable phenomenon. In this paper we initiate the computational study of majority illusion in social networks, providing NP-hardness and parametrised complexity results for its occurrence and elimination.

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Published

2023-06-26

How to Cite

Grandi, U., Kanesh, L., Lisowski, G., Sridharan, R., & Turrini, P. (2023). Identifying and Eliminating Majority Illusion in Social Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 37(4), 5062-5069. https://doi.org/10.1609/aaai.v37i4.25634

Issue

Section

AAAI Technical Track on Domain(s) of Application