Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Opinion Dynamics with Local Interactions
Authors: Dimitris Fotakis, Dimitris Palyvos-Giannas, Stratis Skoulakis
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental ๏ฌndings indicate that for a wide range of parameters, the convergence time and the number of opinion clusters of the neighborhood-restricted variants are comparable to those of the standard Hegselmann-Krause model. |
| Researcher Affiliation | Academia | Dimitris Fotakis and Dimitris Palyvos-Giannas and Stratis Skoulakis National Technical University of Athens EMAIL, EMAIL, EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks were found. |
| Open Source Code | No | No explicit statement or link for open-source code for the described methodology was found. |
| Open Datasets | Yes | Finally, we test the network-HK model on the Facebook circles network [Leskovec, 2012], with 4039 nodes and 88234 edges (see Figure 6a). |
| Dataset Splits | No | The paper does not provide specific train/test/validation dataset splits. |
| Hardware Specification | No | No specific hardware (GPU/CPU models, memory, etc.) used for running experiments is mentioned. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned. |
| Experiment Setup | Yes | We simulate the random-HK and the standard HK models for 625 agents and 21 values of " \in [.01, .45], repeating each run for 100 different initial opinion vectors to account for the randomness in local interaction. Opinions are selected uniformly at random from [0, 1] in all our simulations. We choose k = min(n/10, log n/") to ensure that with high probability every agent has some neighbors in each cluster. |