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title: graphlayouts - Additional Layout Algorithms for Network Visualizations
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## Description
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# graphlayouts - Additional Layout Algorithms for Network Visualizations
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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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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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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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@@ -22,55 +48,150 @@ Some more specific algorithms allow the user to emphasize hidden group structure
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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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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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`graphlayouts` accepts `igraph` network objects as input and includes many datasets that can be used to test the layout algorithms.
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## Output Data
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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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The output of `graphlayouts` is a layout object created with `create_layout()` from `ggraph`.
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This object is essentially a data frame that contains the x–y coordinates of each node, along with any node attributes from the input graph. It can be directly passed into plotting functions in `ggraph`.
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## Environment Setup
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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 (Optional) -->
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## How to Use
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After installing the package and its dependencies you can start visualizing networks with `graphlayouts` and the `ggraph` plotting framework.
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## Input Data
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### Create or load a graph
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Graphs can be created manually or loaded from existing datasets. For example:
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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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```r
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library(igraph)
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g<- make_ring(10) # simple ring graph with 10 nodes
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```
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<!-- This is an example -->
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### Choose a layout algorithm
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The core contribution of `graphlayouts` is a collection of layout algorithms not included in base `igraph`. Layouts are deterministic (they give the same result each time) and often based on *stress majorization*, which aims to preserve graph-theoretic distances in 2D space.
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`graphlayouts` accepts `igraph` network objects as input.
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Use `create_layout()` from `ggraph` with one of the following layout options provided by `graphlayouts`:
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-`"stress"` – general-purpose layout that minimizes stress in node placement.
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-`"focus"` – emphasizes the position of one or more focal nodes.
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-`"backbone"` – highlights the backbone structure of a network while de-emphasizing weaker connections.
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-`"centrality"` – arranges nodes according to a chosen centrality measure (e.g., degree or betweenness).
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## Sample Input and Output Data
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```r
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library(graphlayouts)
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library(ggraph)
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The R package `networkdata` includes many datasets that can be used to test the layout algorithms.
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lay<- create_layout(g, layout="stress")
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```
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## How to Use
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### Draw the network with `ggraph`
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With a layout prepared, you can visualize the graph using `ggraph` layers.
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You can find a tutorial [here](ADD METHODS LINK)
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-**Nodes**: use `geom_node_point()` for basic dots, `geom_node_text()` for labels, or scale aesthetics (size, color, fill) by node attributes.
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-**Edges**: use `geom_edge_link()` for straight lines, `geom_edge_arc()` for curved arcs, or directional edges with arrows.
-**Edge thickness or color** can map to weights or categories.
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-**Labels** can be added with `geom_node_text()` or `geom_node_label()`.
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-**Themes and palettes**: use `theme_graph()` for a clean default or apply any ggplot2 scales (e.g., `scale_fill_viridis_c()`).
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### Explore alternatives
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`graphlayouts` is particularly useful when different research questions call for different visual emphases:
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- Use `"focus"` layouts to visualize networks from the perspective of a key actor.
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- Use `"backbone"` layouts to highlight community structures.
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- Compare multiple layouts to see how structural features (clusters, bridges, hubs) appear under different perspectives.
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You can find a complete tutorial [here](https://methodshub.gesis.org/library/tutorials/network-visualizations-in-r/)
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## Technical Details
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<!-- ## Publication -->
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<!-- - Include information on publications or articles related to the method, if applicable. -->
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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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<!-- ## Acknowledgements -->
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<!-- - Acknowledgements if any -->
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<!-- ## Disclaimer-->
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## Contact Details
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