Election Control in Social Networks via Edge Addition or Removal
Authors: Matteo Castiglioni, Diodato Ferraioli, Nicola Gatti1878-1885
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a positive result, showing that, except for trivial cases, manipulation is not affordable, the optimization problem being hard even if the manipulator has an unlimited budget (i.e., he can add or remove as many edges as desired). Furthermore, we prove that our hardness results still hold in a reoptimization variant, where the manipulator already knows an optimal solution to the problem and needs to compute a new solution once a local modiļ¬cation occurs (e.g., in bandit scenarios where estimations related to random variables change over time).The hardness results presented in this work are a starting point for shaping the landscape of manipulability of election through social networks. |
| Researcher Affiliation | Academia | 1Politecnico di Milano, Piazza Leonardo da Vinci 32, Milano, Italy 2Universit a degli Studi di Salerno, Via Giovanni Paolo II, Fisciano, Italy |
| Pseudocode | No | The paper describes problem definitions and theoretical proofs but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of source code for its methodology. |
| Open Datasets | No | The paper is theoretical and does not involve the use of datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits (e.g., training, validation, test). |
| Hardware Specification | No | The paper is theoretical and does not describe specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |