Add a new text file or a PDF document and visualize it as a network graph

Text network visualization can be useful for better understanding long texts or getting a different perspective on your writing and research.

The graph will quickly show the main topics, how they are related, as well as the structural gaps in the discourse to give you some hints to develop it further.

To visualize a new text or a document:

  1. Open the top left menu and click +add new graph, give a name to your new text and click the save button:



Alternatively, you can go directly to InfraNodus Apps and then click the Advanced Import app button:



  1. Then click the + File button at the edit pane at the bottom left. If you don’t see the edit pane, click the show statements button at the top right corner:



  1. You will find yourself on the Import File page. Click Choose file to select the file you want to visualize from your disk, check that it’s a txt or a pdf file, check the name of the new graph, and click Save:



  1. After a few seconds you will see a new page. If your file was big, you might have to reload it after a few seconds to see the full graph. To do that, click Ctrl+R (Cmd + R on Mac) or click the button. You will see the full graph:



  1. The bigger nodes are the more influential nodes. The clusters of nodes (words) that tend to co-occur together are positioned closer to each other on the graph and are designated with distinct colors.

  1. You might want to hide the text data by clicking the Graph menu at the top left and also minimize the edit pane by clicking the minimize icon:



  1. You might also want to filter the graph — for example, remove the words that are not relevant, such as fig, rev, and doi in this example above (it’s a scientific paper, so these words are often mentioned but are not relevant), plus the name of the author. In order to do it, click on the nodes and add them into the stopwords list:



  1. You can then click the Essence pane at the top to see the most important keywords and topical clusters in this document:


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