Gephi vs yEd Graph Editor

Struggling to choose between Gephi and yEd Graph Editor? Both products offer unique advantages, making it a tough decision.

Gephi is a Data Visualization solution with tags like graph-theory, data-mining, social-network-analysis, open-source.

It boasts features such as Interactive visualization and exploration of network graphs, Statistical analysis tools to examine network structure and content, Algorithms for network clustering, ranking, and layout, Filtering, manipulation and partitioning of graphs, Dynamic filtering during visualization, Generation of high-quality graphical renderings for publication and pros including Free and open source, Support for large network datasets, Plugin architecture for extensibility, Cross-platform compatibility, Intuitive and flexible user interface.

On the other hand, yEd Graph Editor is a Office & Productivity product tagged with diagram, flowchart, network-diagram, uml, bpmn, organization-chart.

Its standout features include Automatic layout algorithms, Support for many diagram types like flowcharts, network diagrams, UML diagrams, BPMN diagrams, org charts, Drag-and-drop interface, Customizable templates, Export to PNG, JPG, SVG, PDF formats, Real-time collaboration, Tree, mindmap, matrix and graph support, Customizable appearance and themes, Zooming and panning, Search and filter, Undo/redo, and it shines with pros like Free and open source, Intuitive and easy to use, Powerful automatic layouts, Extensive diagramming capabilities, Cross-platform availability.

To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.

Gephi

Gephi

Gephi is an open-source network analysis and visualization software package. It allows users to interactively visualize and explore network graphs, run statistical analysis on the structure and content of the networks, and generate high-quality graphical renderings for publications.

Categories:
graph-theory data-mining social-network-analysis open-source

Gephi Features

  1. Interactive visualization and exploration of network graphs
  2. Statistical analysis tools to examine network structure and content
  3. Algorithms for network clustering, ranking, and layout
  4. Filtering, manipulation and partitioning of graphs
  5. Dynamic filtering during visualization
  6. Generation of high-quality graphical renderings for publication

Pricing

  • Open Source

Pros

Free and open source

Support for large network datasets

Plugin architecture for extensibility

Cross-platform compatibility

Intuitive and flexible user interface

Cons

Steep learning curve

Limited native statistical analysis features

Exporting high-quality images can be challenging

Less active development compared to alternatives


yEd Graph Editor

yEd Graph Editor

yEd is a free and open-source diagramming software for Windows, macOS, and Linux. It allows users to quickly and easily create diagrams like flowcharts, network diagrams, UML diagrams, BPMN diagrams, org charts, and more. yEd has automatic layout algorithms to tidy up diagram layouts.

Categories:
diagram flowchart network-diagram uml bpmn organization-chart

YEd Graph Editor Features

  1. Automatic layout algorithms
  2. Support for many diagram types like flowcharts, network diagrams, UML diagrams, BPMN diagrams, org charts
  3. Drag-and-drop interface
  4. Customizable templates
  5. Export to PNG, JPG, SVG, PDF formats
  6. Real-time collaboration
  7. Tree, mindmap, matrix and graph support
  8. Customizable appearance and themes
  9. Zooming and panning
  10. Search and filter
  11. Undo/redo

Pricing

  • Free
  • Open Source

Pros

Free and open source

Intuitive and easy to use

Powerful automatic layouts

Extensive diagramming capabilities

Cross-platform availability

Cons

Limited customization compared to paid alternatives

No native Visio import/export

Steep learning curve for advanced features