Election Manipulation on Social Networks: Seeding, Edge Removal, Edge Addition
Authors: Matteo Castiglioni, Diodato Ferraioli, Nicola Gatti, Giulia Landriani
JAIR 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a comprehensive theoretical study of the election control problem, investigating two forms of manipulations: seeding to buy influencers given a social network and removing or adding edges in the social network given the set of the seeds and the information sent. In particular, we study a wide range of cases distinguishing in the number of candidates or the kind of information spread over the network. Our main result shows that the election manipulation problem is not affordable in the worst-case, even when one accepts to get an approximation of the optimal margin of victory, except for the case of seeding when the number of hard-to-manipulate voters is not too large, and the number of uncertain voters is not too small, where we say that a voter that does not vote for the manipulator s candidate is hard-to-manipulate if there is no way to make her vote for this candidate, and uncertain otherwise. |
| Researcher Affiliation | Academia | Matteo Castiglioni EMAIL DEIB, Politecnico di Milano Piazza Leonardo da Vinci, 32 20133, Milano (MI), Italy Diodato Ferraioli EMAIL DIEM, Universit a degli Studi di Salerno Via Giovanni Paolo II, 132 84084, Fisciano (SA), Italy Nicola Gatti EMAIL Giulia Landriani EMAIL DEIB, Politecnico di Milano Piazza Leonardo da Vinci, 32 20133, Milano (MI), Italy |
| Pseudocode | No | The paper describes algorithms (e.g., 'greedy polynomial-time algorithm') and their properties but does not present them in structured pseudocode or algorithm blocks. The description of algorithms is embedded within the text. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is a theoretical study and does not use or refer to any specific datasets that are publicly available. Examples are constructed conceptually (e.g., Figure 1: 'Clique with five voters and five candidates'). |
| Dataset Splits | No | The paper is a theoretical study and does not use or analyze any datasets, therefore it does not mention any dataset splits (training, validation, test). |
| Hardware Specification | No | The paper focuses on theoretical complexity analysis and algorithm design. It does not describe any experimental evaluations that would require specific hardware, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is a theoretical study focused on complexity and algorithm design. It does not mention any specific software (libraries, frameworks, or solvers) or their version numbers used for implementation or experimentation. |
| Experiment Setup | No | The paper is theoretical and provides complexity results and approximation bounds for algorithms. It does not describe practical experiments with hyperparameters or system-level training settings. |