Biased Majority Opinion Dynamics: Exploiting Graph k-domination
Authors: Hicham Lesfari, Frédéric Giroire, Stéphane Pérennes
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this work, we aim at contributing to the general understanding of the evolution of biased opinion dynamics under the non-linear majority rule by studying their behavior theoretically and empirically. We make the following contributions: Finally, we support our theoretical findings by consistent experiments, relating the speed of consensus with properties of the network structures. In this section, we present and discuss experiments on the stabilization time and the existence of Decreasing structures. |
| Researcher Affiliation | Academia | Hicham Lesfari , Fr ed eric Giroire , St ephane P erennes Universit e Cˆote d Azur, Inria, CNRS, I3S, Sophia Antipolis, France {hicham.lesfari, frederic.giroire, stephane.perennes}@inria.fr |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper uses generated random graph models for experiments (e.g., 'random -regular network'), not publicly available datasets with specific access information or citations. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce data partitioning for train/validation/test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. It only mentions computation time. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | In Figure 2, we plot τα(G) in a random -regular network of size n = 1000 as a function of 1α. Each experiment over G is averaged over 10 iterations and terminated if τα(G) bypasses 1010 iterations (which requires a prohibitive computation time of 71 hours). In Figure 3, we visualize the impact of the size n on stabilization in a random 5-regular network. Each experiment over G is averaged over 500 (resp. 5) iterations for every α in RF (resp. RS). |