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Copy file name to clipboardExpand all lines: methodshub.qmd
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title: graphlayouts - Additional Layout Algorithms for Network Visualizations
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---
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## Description
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# graphlayouts - Additional Layout Algorithms for Network Visualizations
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<!--
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General specifications:
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- This specification of the Methods Hub friendly README often uses the word 'should' to indicate the usual case. If you feel you need to do it differently, add a comment to argue for your case when you submit your method.
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- A Methods Hub friendly README should contain all sections below that are not marked as optional, and can contain more sections.
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- A Methods Hub friendly README should contain as few technical terms as possible and explain (or link to an explanation of) all used technical terms.
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- A Methods Hub friendly README should link to all code files that it mentions using the [text](URL relative to this file) format. The relative URL (i.e., no "https://github.com") is neccessary for proper versioning in Methods Hub.
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- A Methods Hub friendly README should contain an explanation (in the text) and an alternative for each image it contains (e.g., data models, pipeline, schema structure). Format: .
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- A Methods Hub friendly README should link to authoritative sources rather than containing a copy of the information (e.g., documentation).
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- A Methods Hub friendly README should use a uniform citation style for all references, for example APA7 https://apastyle.apa.org/style-grammar-guidelines/references/examples
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Title:
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1. The title must be the README's only first-level heading (line starting with a single '#').
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2. The title should make the method's purpose clear.
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3. The title (line 1 of this file) must be changed by you, but all other headings should be kept as they are.
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4. The title must be appropriate (not harmful, derogatory, etc.).
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Section templates:
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The README template comes with text templates for each section (after each comment) that can be used, customized or removed as desired.
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-->
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<!-- - Provide a brief and clear description of the method, its purpose, and what it aims to achieve. Add a link to a related paper from social science domain and show how your method can be applied to solve that research question. -->
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## Description
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<!--
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1. Provide a brief and exact description of the method clearly mentioning its purpose i.e., what the method does or aims to achieve in abstract terms (avoiding technical details).
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2. The focus should be on explaining the method in a way that helps users with different levels of expertise understand what it does, without going into technical details. It should clearly describe what inputs are needed and what outputs can be expected.
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3. Briefly explain the input and output of the method and its note worthy features.
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4. Provide link(s) to related papers from the social science domain using the method or similar methods for solving social science research questions.
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5. In a separate paragraph, highlight the reproducibility aspect of the method providing details or references to the resources used by the method, the data used in building the pre-trained modules etc.
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The package implements several new layout algorithms to visualize networks are provided which are not part of 'igraph'.
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Most are based on the concept of stress majorization by Gansner et al. (2004) <doi:10.1007/978-3-540-31843-9_25>.
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Some more specific algorithms allow the user to emphasize hidden group structures in networks or focus on specific nodes.
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Some more specific algorithms allow the user to emphasize hidden group structures in networks or focus on specific nodes..
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## Keywords
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* Network Visualization
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* Network layouts
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## Science Usecase(s)
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## Use Cases
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1. The use cases section should contain a list of use cases relevant to the social sciences.
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2. Each use case should start with a description of a task and then detail how one can use the method to assist in the task.
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3. Each use case may list publications in which the use case occurs (e.g., in APA7 style, https://apastyle.apa.org/style-grammar-guidelines/references/examples).
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-->
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Network visualization offers social scientists a powerful tool for analyzing relationships and interactions within digital traces. For instance, researchers studying online communities can use network visualization to map interactions on social media platforms, such as X or Reddit. By visualizing user interactions (like replies, mentions, or shared links), researchers can uncover patterns in information flow, identify influential users, and explore the formation of communities or echo chambers. Network visualization can reveal clusters of users who frequently engage with one another, suggesting tightly-knit subgroups with shared interests or beliefs. It also helps identify key influencers within these networks, who may play a critical role in spreading information or shaping public opinion. This analysis is particularly useful for understanding phenomena like misinformation spread, public discourse on sensitive topics, or the social dynamics of online activism, offering insights into how ideas and behaviors propagate through digital spaces.
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## Repository structure
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## Input Data
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1. The input data section should illustrate the input data format by showing a (possibly abbreviated) example item and explaining (or linking to an explanation of) the data fields.
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2. The input data section should specify which parts of the input data are optional and what effect it has to not provide these.
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3. The input data section should link to a small example input file in the same repository that can be used to test the method (this test should be described in the section "How to Use").
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This repository follows [the standard structure of an R package](https://cran.r-project.org/doc/FAQ/R-exts.html#Package-structure).
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`graphlayouts` accepts `igraph` network objects as input and includes many datasets that can be used to test the layout algorithms.
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## Environment Setup
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## Output Data
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1. The output data section should illustrate the output data format by showing a (possibly abbreviated) example item and explaining (or linking to an explanation of) the data fields.
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2. The output data section should link to a small example output file in the same repository that can be re-created (as far as the method is non-random) from the input data (as described in the section "How to Use").
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With R installed:
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```r
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install.packages("graphlayouts")
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```
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## Hardware Requirements
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Graphlayouts runs on any machine that can run R.
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## Input Data
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<!-- - The input data has to be a Digital Behavioral Data (DBD) Dataset -->
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<!-- - You can provide link to a public DBD dataset. GESIS DBD datasets (https://www.gesis.org/en/institute/digital-behavioral-data) -->
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Graphlayouts runs on any hardware that can run R.
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<!-- This is an example -->
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`graphlayouts` accepts `igraph` network objects as input.
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## Environment Setup
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## Sample Input and Output Data
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With R installed:
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The R package `networkdata` includes many datasets that can be used to test the layout algorithms.
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```r
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install.packages("graphlayouts")
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```
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## How to Use
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1. The how to use section should provide the list of steps that are necessary to produce the example output file (see section Output Data) after having set up the environment (see section Environment Setup).
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You can find a tutorial [here](ADD METHODS LINK)
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## Technical Details
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2. In case a publication provides the details mentioned below, the technical details section should link to this publication using a sentence like "See the [publication](url-of-publication-best-using-doi) for ...". In this case, the mentioned technical details can be omitted from the section.
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3. The technical details section should list all information needed to reproduce the method, including employed other methods and selected parameters.
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See the official [CRAN page](https://doi.org/10.32614/CRAN.package.graphlayouts) for further information about technical details.
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<!--## References -->
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1. The references section is optional, especially if they are cited in a publication that explains the technical details (see section Technical Details).
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<!-- ## Acknowledgements -->
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Check the official [CRAN page](https://doi.org/10.32614/CRAN.package.graphlayouts) for further information.
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<!-- ## Disclaimer-->
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## Contact Details
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1. The contact details section should specify whom to contact for questions or contributions and how (can be separate entitites; for example email addresses or links to the GitHub issue board).
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