Heuristics for Opinion Diffusion via Local Elections

Rica Gonen, Martin Koutecký, Roei Menashof, Nimrod Talmon

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


Most research on influence maximization considers asimple diffusion model, in which binary information is being diffused (i.e., vertices – corresponding to agents – are either active or passive). Here we consider a more involved model of opinion diffusion: In our model, each vertex in the network has either approval-based or ordinal-based preferences and we consider diffusion processes in which each vertex is influenced by its neighborhood following a local election, according to certain “local” voting rules. We are interested in externally changing the preferences of certain vertices (i.e., campaigning) in order to influence the resulting election, whose winner is decided according to some “global” voting rule, operating after the diffusion converges. As the corresponding combinatorial problem is computationally intractable in general, and as we wish to incorporate probabilistic diffusion processes, we consider classic heuristics adapted to our setting: A greedy heuristic and a local search heuristic. We study their properties for plurality elections, approval elections, and ordinal elections, and evaluate their quality experimentally. The bottom line of our experiments is that the heuristics we propose perform reasonably well on both the real world and synthetic instances. Moreover, examining our results in detail also shows how the different parameters (ballot type, bribery type, graph structure, number of voters and candidates, etc.) influence the run time and quality of solutions. This knowledge can guide further research and applications.

Original languageEnglish
Title of host publicationSOFSEM 2023
Subtitle of host publicationTheory and Practice of Computer Science - 48th International Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2023, Proceedings
EditorsLeszek Gasieniec
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages15
ISBN (Print)9783031231001
StatePublished - 2023
Event48th International Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2023 - Nový Smokovec, Slovakia
Duration: 15 Jan 202318 Jan 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13878 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference48th International Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2023
CityNový Smokovec

Bibliographical note

Funding Information:
Partially supported by Ministry of Science, Technology and Space Binational Israel-Taiwan grant, number 3-16542. Partially supported by Charles University project UNCE/SCI/004 and by the project 22-22997S of GA ČR. Computational resources were supplied by the project “e-Infrastruktura CZ" (e-INFRA CZ LM2018140) supported by the Ministry of Education, Youth and Sports of the Czech Republic, and by the ELIXIR-CZ project (LM2018131), part of the international ELIXIR infrastructure.

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.


  • Bribery in elections
  • Influence maximization
  • Social choice


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