On a Voter Model with Context-Dependent Opinion Adoption
Authors: Luca Becchetti, Vincenzo Bonifaci, Emilio Cruciani, Francesco Pasquale
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose and study a context-dependent opinion spreading process on an arbitrary social graph, in which the probability that an agent abandons opinion a in favor of opinion b depends on both a and b. We discuss the relations of the model with existing voter models and then derive theoretical results for both the fixation probability and the expected consensus time for two opinions, for both the synchronous and the asynchronous update models. |
| Researcher Affiliation | Academia | 1Sapienza University of Rome Rome, Italy 2Roma Tre University Rome, Italy 3Paris-Lodron University of Salzburg Salzburg, Austria 4Tor Vergata University Rome, Italy |
| Pseudocode | Yes | Algorithm 1 Update(u) 1: Sample v N(u) 2: c xu; c xv 3: Sample θ [0, 1] 4: if θ < αc,c then 5: xu xv 6: return accept 7: return reject |
| Open Source Code | No | The paper provides a link to an arXiv pre-print for the 'full paper version' (http://arxiv.org/abs/2305.07377) but does not provide any links or statements for open-source code related to the methodology described. |
| Open Datasets | No | The paper focuses on theoretical analysis and does not use or refer to any datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe any dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental setup, therefore no software dependencies are mentioned. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical derivations and analysis. It does not describe an experimental setup with hyperparameters or training settings. |