Network Visualization and Analysis Using InfraNodus


Using InfraNodus you can either create a new graph very easily using the text-to-graph input or import your own existing graph: for example, a Gephi gexf graph or a graph saved in a CSV file.

You can then obtain the graph analytics for all the nodes and the network structure in general. You can also save and export the graph for further exploration in other platforms, such as Gephi and Pajek, or as an image to add it into your work. Graphs that are made public can also be embedded or shared via a URL.

This interactive graph below made using InfraNodus showcases an example graph of the relations between the organs in TCM. You can see the main nodes on the graph (high betweenness centrality — bigger on the graph) as well as the main communities (clusters) and the statistics on every node and on the network in the graph analytics panel (right). Feel free to play around with this graph to see how it works.




Network Visualization and Analysis Workflow

Here is how you can visualize your own graph or data using InfraNodus

You can import a graph from Gephi or any other software (Gexf or CSV formats) or simply create your graph yourself. You can also import the data from Twitter, Excel and other sources.

Create a new mind map graph

When you are building your own graph, you can use the #hashtags and @mentions to add the nodes (@ links to all the #). The rest of the text is a description of the relation. For example, the phrase "@liver is responsible for #metabolism and cleans #blood" will create 3 nodes and link the ones marked with a # sign to the one marked with the @ sign.

Use the text-to-graph input to create your network visualization

You will then see the most influential nodes (betweenness centality) and the most prominent communities. All the stats for the nodes and the graph structure are available in the Analytics panel.

See your mind map as a network graph.

Use the Insight feature to get structural insights into the network: what are the main gaps and what nodes and communities could be bridged together.

Structural holes in knowledge graphs generate new ideas

Network analysis methods implemented in InfraNodus are based on the reputable open-source libraries, such as Cytoscape and Graphology. Our approach is described in detail in our peer-reviewed paper: InfraNodus — Geneating Insight Using Text Network Analysis (Paranyushkin, 2019) presented at The Web Conference in 2019. You can refer to this paper for more detail on the algorithms used or for citation.

Once you import a graph, you can then use the Analytics panel to get additional insights about the network graph: the most influential nodes, the most prominent communities of nodes, and the relations between them.

You will also get special insights about the structure of the network: whether it's well connected or sparse, which has implications on the speed of information proliferation within this network.

All the graph and all the data can be exported or shared as a URL or Embed. Your graphs are private by default, so you would need to make them public to do so, or simply export a PNG image if you'd like to add a visualization on your website or into your research paper.




Network Visualization and Analysis Tools: Comparison Graph


Here is a graph of the most interesting tools for network visualization and analysis, as well as the functionalities they have that shows how they relate to each other. It is made using InfraNodus and you can see how it can apply graph metrics to mindmaps as well:




Try It Yourself


You can try this approach yourself using InfraNodus for any text or graph data:


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Custom Service: Network Visualization


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