Pairwise Diffusion of Preference Rankings in Social Networks
Authors: Markus Brill, Edith Elkind, Ulle Endriss, Umberto Grandi
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce a model of preference diffusion in which agents in a social network update their preferences based on those of their influencers in the network, and we study the dynamics of this model.In this paper we identify conditions for a process of preference diffusion in our model to terminate, and we seek to characterise the profiles of preferences the process converges to in case of termination. |
| Researcher Affiliation | Academia | Markus Brill University of Oxford United Kingdom mbrill@cs.ox.ac.uk Edith Elkind University of Oxford United Kingdom elkind@cs.ox.ac.uk Ulle Endriss University of Amsterdam The Netherlands ulle.endriss@uva.nl Umberto Grandi University of Toulouse France umberto.grandi@irit.fr |
| Pseudocode | No | The paper describes the PPD update function and diffusion processes in narrative text and mathematical notation, but it does not include a structured pseudocode block or a clearly labeled algorithm figure. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or links to a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on a new model and its properties; it does not involve experiments with datasets, thus no training data is mentioned or made available. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments or dataset evaluation, therefore no training, validation, or test splits are mentioned. |
| Hardware Specification | No | The paper describes a theoretical model and its properties; it does not involve computational experiments, and therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe implementation details or computational experiments, therefore no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and does not involve empirical experiments, thus no experimental setup details, hyperparameters, or system-level training settings are provided. |