InfraNodus: Text Network Analysis and Visualization Tool

Represent your text as a network and apply advanced graph theory tools for topic modeling, classification, and structural gap detection.



Text mining and topic modeling using network analysis

Focus on the Relations in Your Text

Use advanced network science metrics, such as betweenness centrality and modularity to identify the most important topics in your text and the relations between them.

1. Import Your Spreadsheets,
PDF, or Text Data

You can use CSV spreadsheets, PDF / text files, or create your graphs from scratch using our text-to-graph editor, which is blazingly fast.

2. Apply Network Science to
Get an Overview & Tag Your Data

Apply force-layout and modularity algorithms to detect topic clusters and tag your text statements. Range the nodes by their influence using betweenness centrality.

3. Use Open AI GPT
to Generate Insight

Use the network representation to find the structural gaps in your discourse. Then apply the built-in AI to generate research questions to bridge those gaps.

Advanced Statistics for Your Machine Learning Models

Calculate and export advanced statistical data on your text and every statement. Tag every statement for further use.

Interactive Text Network Graphs

Use the graph to read through your texts visually, find relevant excerpts, analyze relations. Slice through your texts to reveal the underlying details.

Fully Shareable

You can link to your graphs, embed them to your site, and export in gexf, png, csv, text formats.

Private by Default

All your data is private by default, stored on EU servers, and you can export and erase it anytime.


Create Your Own Text Graphs

Sign up for an account now, so you can try how it works. We offer a 14-day free trial, so you don't have to pay anything if InfraNodus doesn't live up to your expectations.

How Text Network Analysis Works

Text network analysis and graph visualization represents a text as a network. The words (or lemmas) are the nodes, their co-occurrences are the relations between them. Represented this way, you can then align the words / nodes that tend to co-occur together as clusters in a 2D plane, which helps you identify the topical clusters, the relations between them, and the most influential nodes. You can also get insight into the structure of a discourse.

Check out the links to our research papers below or this brief explanation of how it works:


1. Add Your Text

Start with your own text, e.g. a research paper, a book, a PDF, or your research notes. You can also import Google Scholar search results using InfraNodus import apps.

If you have your data in a spreadsheet, save it as CSV and use InfraNodus CSV import app to visualize this data as a graph.

You can also create the graph manually with a plain text editor and it is the fastest graph creation tool on the market. Use plain text or #hasthags to add nodes, their co-occurrences will be represented as the connections.

More in our Whitepaper
Create a Text Network Graph

2. Text Network Graph Visualization

Text network will then be generated from the text or data you added. The most influential words in the graph (the nodes with the highest betweenness centrality) are shown bigger, while the nodes that occur more often together are aligned into topical clusters in a 2D plane and have distinct colors.

The graph will show you not only the main clusters and the most influential terms, but also the relations and gaps between them.

The topics identified using this approach are much more precise than LDA-based methods. Your text statements will be tagged with those topics, so you can export them for further analysis or machine-learning models.

Overview Data with Text Network Visualization

3. Generate Insight using AI
and Tag Your Text Data

InfraNodus will identify the structural gaps in the network: parts of the graph that could be connected but are not.

Use the graph or our built-in GPT based AI model to bridge these gaps and to connect your ideas in a new way.

You can also export your text statements tagged with the topics that they belong to as a CSV file (with InfraNodus insights) for further analysis or use them as a training data set for your neural network applications.

Generating Insight with Structural Gaps

Our Methodology and Citations

To learn more about the algorithms used in InfraNodus and for citations, please, refer to this peer-reviewed paper:

Paranyushkin, D (2019). InfraNodus: Generating Insight Using Text Network Analysis, Proceedings of WWW'19 The Web Conference, (ACM library, PDF).

If you want to know more about the algorithms used, here is the first paper we published on the subject:

Paranyushkin, D (2011). Identifying the pathways for meaning circulation using text network analysis, Nodus Labs. (Google scholar)

Become a Supporter!

InfraNodus is developed without external funds or investors and we want to keep it affordable for individuals and small businesses (unlike other tools that start at 30K a year). We develop and maintain this tool and the associated research based on your contributions only. We believe it is a working model for crowdfunded software made by users for users.

You can support our work if you become a subscriber and get access to the cloud version or if you become our patron on Patreon. Enterpise, government, and educational versions are also available.

Pricing Options

This tool and the associated research in ecological network thinking are developed solely on the monthly contribution provided by users like you. We believe this is a working model for crowd-funded R&D, and we thank you for your support!

All quotas are per each text. There is no limitation on the number of texts you can process every month.

About InfraNodus

Gregory Bateson coined a beautiful term: "ecology of the mind". What is a mind that is ecological? It has the ability to have an overview, but it can also zoom into any idea. It embraces diversity, but it can also obsess over one thing when needed. It can discover the obvious, but it can also reveal the things that are hidden and ponder the gaps that have not yet been bridged. Rational and poetic.

InfraNodus is a tool that is developed to help you think this way. It is made to promote ecological dynamics and diversity on the cognitive level. InfraNodus visualises and analyses ideas as a network, revealing the relations and patterns within them, so you can understand the dynamic complexity of how knowledge evolves and explore the nuances of meaning.

We believe that it is especially important today to make this kind of instruments available to everybody. That's why we develop InfraNodus so that it can accessible to everyone, solely thanks to our users' monthly contributions and based on their feedback. This is not a start-up, we don't have investors and an exit strategy. We are here to stay and to evolve.

We are constantly improving InfraNodus based on our users' feedback. We are also currently developing an API to make this way of thinking available to other applications and to enable the use of InfraNodus insights for machine learning and AI applications.

If you like our cause, please, sign up for an account and give it a try, support us on Patreon, or simply let us know that it resonates, because this is the best way to motivate each other.

Dmitry Paranyushkin, the founder




InfraNodus is used by researchers, writers, marketing professionals and corporations. Here's what some of our supporters have to say:

Technical Support

Check out our support pages, so you can see how to create the new network graphs, how to read and interpret them, how to discover the hidden patterns in your data and share the results of your research.

1. How to Create a New Graph

The most basic way to create a new graph is to use the hashtags. Each hashtag is a node, their co-occurrence is the connection between them. You can also use normal text to build your text graphs automatically. More on Creating the Graphs

2. How to Read and Interpret Network Visualization

Studies have shown that diagrams and especially network representation of data makes you think more of connections in your data. You can identify the main elements as well as the relations between them and — most importantly — discover the gaps in your knowledge using the graphs. More on Reading the Graphs

3. Find the Right Excerpt in Text

Network graphs are great for non-linear reading. Look at the graph, find the part you like, click on the nodes, and you'll see the excerpt of your original data that contains this exact combination. More on Nonlinear Reading

4. How to Import Text and PDF files

You can import and visualize your text files or PDF documents. In order to do that, just upload the file and InfraNodus will do the rest. More on Visualizing PDFs