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Gephi Quick Start
 

Gephi Quick Start

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This is the introduction tutorial. It will guide you to the basic steps of network visualization and manipulation in Gephi.

This is the introduction tutorial. It will guide you to the basic steps of network visualization and manipulation in Gephi.

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    Gephi Quick Start Gephi Quick Start Presentation Transcript

    • Tutorial Quick Start * Introduction Gephi Tutorial * * * Import file Visualization Layout Quick Start * Ranking (color) * Metrics Welcome to this introduction tutorial. It will guide you to the basic steps of network * Ranking (size) visualization and manipulation in Gephi. * Layout again * Show labels Gephi version 0.7alpha2 was used to do this tutorial. * Community-detection * Partition Get Gephi * Filter * Preview * Export * Save * Conclusion Last updated March 05th, 2010
    • Tutorial Quick Start Open Graph File * Introduction • Download the file LesMiserables.gexf * Import file * Visualization • In the menubar, go to File Menu and Open... * Layout * Ranking (color) * Metrics * Ranking (size) * Layout again * Show labels * Community-detection * Partition * Filter * Preview * Export Graph Format * Save * Conclusion - GEXF - Tulip TLP - GraphML - CSV - Pajek NET - Compressed ZIP - GDF - GML
    • Tutorial Quick Start Import Report * Introduction • When your filed is opened, the report sum up data found and issues. * Import file - Number of nodes * Visualization - Number of edges * Layout - Type of graph * Ranking (color) * Metrics * Ranking (size) * Layout again * Show labels * Community-detection * Partition * Filter * Preview * Export * Save * Conclusion • Click on OK to validate and see the graph
    • Tutorial Quick Start You should now see a graph * Introduction We imported “Les Miserables” dataset1. Coappearance weighted network of * Import file characters in the novel “Les Miserables” from Victor Hugo. * Visualization * Layout * Ranking (color) * Metrics * Ranking (size) * Layout again * Show labels * Community-detection * Partition * Filter * Preview * Export * Save Nodes position is random at first, so you may see a slighty different representation. * Conclusion 1 D. E. Knuth, The Stanford GraphBase: A Platform for Combinatorial Computing, Addison-Wesley, Reading, MA (1993).
    • Tutorial Quick Start Graph Visualization * Introduction • Use your mouse to move and scale the visualization * Import file - Zoom: Mouse Wheel * Visualization - Pan: Right Mouse Drag * Layout Zoom * Ranking (color) • Locate the “Edge Thickness” slider on the bottom * Metrics * Ranking (size) * Layout again * Show labels * Community-detection Drag * Partition • If you loose your graph, reset the position * Filter * Preview * Export * Save * Conclusion
    • Tutorial Quick Start Layout the graph * Introduction Layout algorithms sets the graph shape, it is the most essential action. * Import file * Visualization • Locate the Layout module, on the left panel. * Layout * Ranking (color) • Choose “Force Atlas” * Metrics * Ranking (size) You can see the layout properties below, leave default values. * Layout again * Show labels * Community-detection • Click on to launch the algorithm * Partition * Filter * Preview * Export * Save * Conclusion Layout algorithms Graphs are usually layouted with “Force-based” algorithms. Their principle is easy, linked nodes attract each other and non-linked nodes are pushed apart.
    • Tutorial Quick Start Control the layout * Introduction The purpose of Layout Properties is to let you control the algorithm in order to make a * Import file aesthetically pleasing representation. * Visualization * Layout • Set the “Repulsion strengh” at 10 000 to expand * Ranking (color) the graph. * Metrics * Ranking (size) • Type “Enter” to validate the changed value. * Layout again * Show labels * Community-detection * Partition * Filter * Preview • And now the algorithm. * Export * Save * Conclusion
    • Tutorial Quick Start You should now see a layouted graph * Introduction * Import file * Visualization * Layout * Ranking (color) * Metrics * Ranking (size) * Layout again * Show labels * Community-detection * Partition * Filter * Preview * Export * Save * Conclusion
    • Tutorial Quick Start Ranking (color) * Introduction Ranking module lets you configure node’s color and size. * Import file * Visualization • Locate Ranking module, in the top left. * Layout * Ranking (color) • Choose “Degree” as a rank parameter. * Metrics * Ranking (size) * Layout again * Show labels You should obtain the configuration panel below: * Community-detection * Partition * Filter • Click on to see the result. * Preview * Export * Save * Conclusion
    • Tutorial Quick Start Let’s configure colors * Introduction * Import file • Move your mouse over the gradient component. * Visualization * Layout * Ranking (color) * Metrics • Double-click on triangles to configure the color * Ranking (size) * Layout again * Show labels * Community-detection * Partition * Filter * Preview * Export * Save Palette * Conclusion Use palette by right-clicking on the panel.
    • Tutorial Quick Start Ranking result table * Introduction You can see rank values by enabling the result table. Valjean has 36 links and is the most * Import file connected node in the network. * Visualization * Layout • Enable table result view at the bottom toolbar * Ranking (color) * Metrics * Ranking (size) • Click again on * Layout again * Show labels * Community-detection * Partition * Filter * Preview * Export * Save * Conclusion
    • Tutorial Quick Start Metrics * Introduction We will calculate the average path length for the network. It computes the path length for * Import file all possibles pairs of nodes and give information about how nodes are close from each other. * Visualization * Layout * Ranking (color) • Locate the Statistics module on the right panel. * Metrics * Ranking (size) * Layout again * Show labels • Click on near “Average Path Length”. * Community-detection * Partition * Filter * Preview * Export * Save Metrics available * Conclusion - Diameter - Betweeness Centrality - Average Path Length - Closeness Centrality - Clustering Coefficient - Eccentricity - PageRank - Community Detection - HITS (Modularity)
    • Tutorial Quick Start Metric settings * Introduction The settings panel immediately appears. * Import file * Visualization * Layout * Ranking (color) * Metrics * Ranking (size) * Layout again * Show labels * Community-detection * Partition * Filter * Preview * Export * Save * Conclusion • Select “Directed” and click on OK to compute the metric.
    • Tutorial Quick Start Metric result * Introduction When finished, * Import file the metric dis- * Visualization plays its result in * Layout a report * Ranking (color) * Metrics * Ranking (size) * Layout again * Show labels * Community-detection * Partition * Filter * Preview * Export * Save * Conclusion
    • Tutorial Quick Start Ranking (size) * Introduction Metrics generates general reports but also results for each node. Thus three new values * Import file have been created by the “Average Path Length” algorithm we ran. * Visualization - Betweeness Centrality * Layout - Closeness Centrality * Ranking (color) - Eccentricity * Metrics * Ranking (size) * Layout again * Show labels • Go back to Ranking * Community-detection * Partition • Select “Betweeness Centrality” in the list. * Filter This metrics indicates influencial nodes for highest * Preview value. * Export * Save * Conclusion
    • Tutorial Quick Start Ranking (size) * Introduction The node’s size will be set now. Colors remain the “Degree” indicator. * Import file * Visualization • Select the diamond icon in the toolbar for size. * Layout * Ranking (color) • Set a min size at 10 and a max size at 50. * Metrics * Ranking (size) * Layout again * Show labels * Community-detection * Partition * Filter * Preview • And click on to see the result. * Export * Save * Conclusion
    • Tutorial Quick Start You should see a colored and sized graph * Introduction * Import file * Visualization * Layout * Ranking (color) * Metrics * Ranking (size) * Layout again * Show labels * Community-detection * Partition * Filter * Preview * Export * Save * Conclusion Color: Degree Size: Betweeness Centrality metric
    • Tutorial Quick Start Layout again * Introduction The layout is not completely satisfying, as big nodes can overlap smaller. * Import file * Visualization The “Force Atlas” algorithm has an option to take node size in account when layouting. * Layout * Ranking (color) * Metrics • Go Back to the Layout panel. * Ranking (size) * Layout again • Check the “Adjust by Sizes” option and run again the * Show labels algorithm for short moment. * Community-detection * Partition • You can see nodes are not overlapping anymore. * Filter * Preview * Export * Save * Conclusion
    • Tutorial Quick Start Show labels * Introduction Let’s explore the network more in details now that colors and size indicates central * Import file nodes. * Visualization * Layout • Display node labels * Ranking (color) * Metrics * Ranking (size) * Layout again * Show labels • Set label size proportional to node size * Community-detection * Partition * Filter * Preview * Export * Save • Set label size with the scale slider * Conclusion
    • Tutorial Quick Start Community detection * Introduction The ability to detect and study communities is central in network analysis. We would like * Import file to colorize clusters in our example. * Visualization * Layout Gephi implements the Louvain method1, available from the Statistics panel. * Ranking (color) Click on near the “Modularity” line * Metrics * Ranking (size) * Layout again • Select “Randomize” on the panel. * Show labels * Community-detection * Partition * Filter • Click on OK to launch the detection. * Preview * Export * Save * Conclusion 1 Blondel V, Guillaume J, Lambiotte R, Mech E (2008) Fast unfolding of communities in large net- works. J Stat Mech: Theory Exp 2008:P10008. (http://findcommunities.googlepages.com)
    • Tutorial Quick Start Partition * Introduction The community detection algorithm created a “Modularity Class” value for each node. * Import file * Visualization The partition module can use this new data to colorize communities. * Layout * Ranking (color) • Locate the Partition module on the left panel. * Metrics • Immediately click on the “Refresh” button to pop- * Ranking (size) ulate the partition list. * Layout again * Show labels * Community-detection * Partition * Filter * Preview * Export * Save How to visualize nodes & edges columns? * Conclusion See columns and values for nodes and edges by looking at the Data Table view. Select Data Laboratory tab and click on “Nodes” to refresh the table.
    • Tutorial Quick Start Partition * Introduction • Select “Modularity Class” in the partition list. * Import file * Visualization You can see that 9 communities were found, could * Layout be different for you. A random color has been set for each community identifier. * Ranking (color) * Metrics * Ranking (size) • Click on to colorize nodes. * Layout again * Show labels * Community-detection * Partition * Filter * Preview * Export Right-click on the panel to access the Randomize colors action. * Save * Conclusion
    • Tutorial Quick Start What the network looks like now * Introduction * Import file * Visualization * Layout * Ranking (color) * Metrics * Ranking (size) * Layout again * Show labels * Community-detection * Partition * Filter * Preview * Export * Save * Conclusion
    • Tutorial Quick Start Filter * Introduction The last manipulation step is filtering. You create filters that can hide nodes and egdes * Import file on the network. We will create a filter to remove leaves, i.e. nodes with a single edge. * Visualization * Layout • Locate the Filters module on the right panel. * Ranking (color) * Metrics • Select “Degree Range” in the “Topology” category. * Ranking (size) * Layout again * Show labels * Community-detection * Partition * Filter • Drag it to the Queries, drop it to “Drag filter here”. * Preview * Export * Save * Conclusion Drag
    • Tutorial Quick Start Filter * Introduction • Click on “Degree Range” to activate the filter. The parameters panel appears. * Import file * Visualization * Layout * Ranking (color) It shows a range slider and the chart that represents the data, the degree distribution * Metrics here. * Ranking (size) * Layout again • Move the slider to sets its lower bound to 2. * Show labels * Community-detection * Partition • Enable filtering by pushing the button. * Filter * Preview * Export Nodes with a degree inferior to 2 are now hidden. * Save * Conclusion Tip You can edit bounds manually by double-clicking on values.
    • Tutorial Quick Start The filtered network * Introduction * Import file * Visualization * Layout * Ranking (color) * Metrics * Ranking (size) * Layout again * Show labels * Community-detection * Partition * Filter * Preview * Export * Save * Conclusion That ends the manipulation. We will now preview the rendering and prepare to export.
    • Tutorial Quick Start Preview * Introduction • Before exporting your graph as a SVG or PDF file, go to the Preview to: * Import file - See exactly how the graph will look like * Visualization - Put the last touch * Layout * Ranking (color) • Select the “Preview” tab in the banner: * Metrics * Ranking (size) * Layout again * Show labels * Community-detection • Click on Refresh to see the preview * Partition * Filter * Preview * Export * Save * Conclusion Tip If the graph is big, reduce the “Preview ratio” slider to 50% or 25% to display a partial graph.
    • Tutorial Quick Start Preview * Introduction • In the Node properties, find “Show Labels” and * Import file enable the option. * Visualization * Layout • Click on * Ranking (color) * Metrics * Ranking (size) * Layout again Preview Settings supports Presets, click on the * Show labels presets list and try different configurations. * Community-detection * Partition * Filter * Preview * Export * Save * Conclusion
    • Tutorial Quick Start The Previewed Graph * Introduction * Import file * Visualization * Layout * Ranking (color) * Metrics * Ranking (size) * Layout again * Show labels * Community-detection * Partition * Filter * Preview * Export * Save * Conclusion
    • Tutorial Quick Start Export as SVG * Introduction From Preview, click on SVG near Export. * Import file * Visualization * Layout * Ranking (color) * Metrics SVG Files are vectorial graphics, like PDF. Images scale smoothly to different sizes and * Ranking (size) can therefore be printed or integrated in high-res presentation. * Layout again * Show labels Transform and manipulate SVG files in Inkscape or Adobe Illustrator. * Community-detection * Partition * Filter * Preview * Export * Save * Conclusion High-resolution screenshots If you prefer hi-resolution PNG screenshots only, look at the icon in the visualization properties bar, located at the bottom of the visualization.
    • Tutorial Quick Start Save your project * Introduction Saving your project encapsulates all data and results in a single * Import file session file. * Visualization * Layout * Ranking (color) * Metrics * Ranking (size) If you missed some steps, you can download the session: * Layout again * Show labels LesMiserables.gephi * Community-detection * Partition * Filter * Preview * Export * Save * Conclusion
    • Tutorial Quick Start Conclusion * Introduction In this tutorial you learned the basic process to open, visualize, manipulate and render * Import file a network file with Gephi. * Visualization * Layout * Ranking (color) * Metrics * Ranking (size) * Layout again * Show labels * Community-detection * Partition * Filter * Preview Go further: * Export • Gephi Website * Save • Gephi Wiki • Gephi forum * Conclusion