Gephi advanced functions Filters, metrics and plugins. Clément Levallois Gephi Support Team and Assist. Business School

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1 Gephi advanced functions Filters, metrics and plugins Clément Levallois Gephi Support Team and Assist. Business School V 1.1 Jan

2 Bio notes Education in economics, management, history of science (Ph.D.) Turned to digital methods for research. data visualization, network analysis, natural language processing, web applications and more. Member of the Gephi Community Support team Gephi certified trainer Contact, feedback welcome: on or

3 FILTERS 3

4 Filters for what goal? 1. Basic function: to hide away a part of the network 2. Advanced use: allow to hide away a part of the network, apply specific parameters to the remaining visible part, and then show the full network hence with a differentiated aspect 4

5 1. Basic use of filters 1. Select a filter in the «catalogue» (Example: «degree range») 2. Drag and drop the filter in the lower part of the filter panel 3. Open its parameters, play with values 4. Click on «Filter» to actually filter 5. Options are made available to save / export / change labels of the filtered graph. 5

6 2. Advanced: modify parameters of the viz Our objective: change the color of a particular group of nodes only 1. Open «Les Misérables.gexf» 2. Select the filter «Modularity class» et drag and drop it. Select a color to filter in. Apply the filter. Filter can be found here: Library -> Attributes -> Partition 3. In the main window, right click on the icon to change node color. Change the default color, confirm. Left click on the same icon to apply this new default color. 4. Stop the filtering by clicking on «Filter» again. Observe how your change of node color has left intact the color of the nodes that had been filtered out. Note: any other operation could have been applied (on size, labels, but also on metrics). 6

7 Combine several filters Most «outwardly» filter, will be applied last «Chained» filters: Drag and drop filters by placing them nested inside each other. The most «inner» filter will be applied first, then the one surrounding it, etc. Frequent use case: nest the filter we are interested in inside a «degree range» filter, of which we set the values to exclude the zero value. Has for effect to hide away the nodes which are isolated (no neighbor) after applying the first filter. Most «inward» filter, will be applied first Filter UNION : Drag and drop the «UNION» filter Insert the filters to apply inside it, without nesting them inside each other. Filter «INTERSECTION»: contradictory report on it functioning or not. Dynamique filter This filter is created automatically by Gephi when the timeline is activated (don t try to apply it yourself anually). Filters to apply to a dynamic graph sould be dragged and dropped, with the dynamic filer always nest at the most inner level of the filters applied.. 7

8 METRICS (STATISTICS) 8

9 Principle Most used algorithms in descriptive statistics for networks are implemented in Gephi Caution: Gephi does not specialize in the complete set of statistics, and has no modelling capacity (it could change! Who want to write a plugin?) See Pajek or UCINET instead. How is Gephi special on statistics: the statistics can be easily visualized on the network, instead of a table of numbers. 9

10 Example calculus of betweenness centrality Preliminary note: A «shortest path» is the shortest way to go from one node to another. Here the shortest path between A and C is A-B-C. A D B E C A node has a strong «betweenness centrality» if: It is situated on many shortest paths 10

11 How to run / use a metrics 1. Run it 2. Check and close the report 3. One or several columns have been added to the datalab. This columns contain the value for the metrics, specific for each node. This means that the result of the metrics has been recorded one can export it and work on it outside of Gephi if needs be. 4. This values can be seen on the graph! Apply a ranking on node size to see which ones are central 11

12 Normalize or not? It is sometimes an option to «normalize» a metrics. This is because metrics can create values with units that are not easy to grasp: (my node has a score of «455» on this metrics: huge or not?). To normalize consists in converting values from 0 to 1. Hence, it is easier to evaluate whether this or that value is close to the minimum value or to the maximum value. Example for betweenness centrality: a normalized value of 0 means that the node is situated on no shortest path, a value of 1 means the node lies on all shortest paths. A non normalized value would show for each node how many shortests paths they lie unto which is hard to judge. 12

13 Directed or not? It matters 1: directed network G F E D C A B Let s Imagine a network of who follows whom ontwitter If we are interested in who has most followers, we should look at the node receiving most links. That s node E. The number of inbound links is called in-degree. It can be computed in Gephi with the Degree metrics. 2: same network, but undirected G E A F D B C Legend: - centrality + Now let s imagine the same network, but imported in Gephi as an undirected network (meaning that the directionality of edges is of no interest). The node with most edges (with the highest degree ) is D (4 connections), not E (just 2 connections). Conclusion: in general, metrics return different results depending on whether the network is considered directed or not. The interpretation can be completely erroneous if one does not take care of this. 13

14 Which centrality measure? It matters! A: degree centrality = local connectivity => nodes with lots of neighbors are central B: closeness centrality = geographic centrality => Nodes at the geographic center are central C: betweenness centrality = transit centrality => Nodes where a lot of transit can happen are central (careful: does not mean transit actually takes place. See Scott Weingart s blog post on this) Légende: D: Eigenvector centrality = authority => Nodes connected to central nodes are central themselves (recursive approach). - centrality + Ref: 14

15 PLUGINS 15

16 Plugins: what for? A plugin brings a functionality which is originally absent from Gephi Once installed, a plugin is totally integrated in Gephi (but it can be uninstalled anytime, of course) It exists around 50 plugins today, new plugins are published regularly. Do we really need them? Yes! As soon as one works in Gephi for a project, one realizes quickly that this or that functionality would have been a life saver. Plugins add these missing functionalities to Gephi: thanks to plugins, Gephi can be extended infinitely. 16

17 Plugins: to insist Plugins are an essential part of a software solution. It is far from trivial to create a system where plugins can be authored, distributed, installed and used reliably. Gephi did it! Plugins easy to create (in Java) Distributed and installed directly from inside Gephi (just need an Internet connection for the download). 17

18 Where to find plugins? In Gephi: Tools -> Plugins -> Available plugins Select a plugin then follow the wizard On the Gephi Marketplace: The Gephi marketplace lists all plugins in detail and give the possibility to download and install them manually. But it remains easier to visit the marketplace website to learn about a plugin, then to go back to Gephi to install it directly (as described above) 18

19 Some very useful plugins (very subjective selection!) Plugins SigmaJS exporter, SeaDragon Web Export, Gexf-JS Web Viewer Fonction 3 plugins to export a Gephi network viz into a web page. Excel / csv converter to network To import non network data, in Excel or csv format. The plugin creates a network and imports it directly into Gephi GeoLayout, Map of countries 2 plugins to visualize geospatial data (GIS like). 19