Persistence of Anti-vaccine Sentiment in Social Networks Through Strategic Interactions
Authors: A S M Ahsan-Ul Haque, Mugdha Thakur, Matthew Bielskas, Achla Marathe, Anil Vullikanti4812-4820
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical analysis. We study the properties of NE in a diverse class of real-world and social networks and random graphs. ... We perform all the experiments using Python 3.7.5 on a Windows 10 Pro machine with 16 GB of physical memory. Networkx was used for graph manipulation, Pandas and Numpy libraries were used for data analysis and Matplotlib for visualization. We consider a variety of synthetic graphs and real-world networks as summarized in Table 2. |
| Researcher Affiliation | Academia | A S M Ahsan-Ul Haque1,2, Mugdha Thakur1, Matthew Bielskas1, 2, Achla Marathe1,3, Anil Vullikanti1,2 1 Biocomplexity Institute, University of Virginia 2 Department of Computer Science, University of Virginia 3 Department of Public Health Sciences, University of Virginia |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the provided text. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We consider a variety of synthetic graphs and real-world networks as summarized in Table 2. ... The social and communication networks were collected from the Stanford Network Analysis Project (Leskovec and Krevl 2014). |
| Dataset Splits | No | The paper does not explicitly provide details about training, validation, or test dataset splits. |
| Hardware Specification | Yes | We perform all the experiments using Python 3.7.5 on a Windows 10 Pro machine with 16 GB of physical memory. |
| Software Dependencies | Yes | We perform all the experiments using Python 3.7.5 on a Windows 10 Pro machine with 16 GB of physical memory. Networkx was used for graph manipulation, Pandas and Numpy libraries were used for data analysis and Matplotlib for visualization. |
| Experiment Setup | Yes | We use a uniform value for C here. ... We set α = δ = 1 and γ = 0.9. ... We compute the worst NE for the datasets summarized in Table 2 using the approach outlined in Theorem 3. |