Infranodus is a revolutionary text network visualization tool that encourages cognitive variability and network heuristics to promote ecological thinking. The tool's key focus is exploring the possibilities and unraveling the world in its polysingularity, enhancing perception by making it adaptable and diverse, ultimately helping users avoid totalitarian thinking. The tool allows you to generate new ideas, visualize text network graphs with its state-of-the-art text-to-network conversion system, and has an Insight Recommender System that proposes an ecological thinking model.
InfraNodus can work on any device, is SEO-optimized so that you can publish and share your work easily, and provides detailed quantitative and qualitative insights. The tool is developed by Nodus Labs and has a rich history of evolution over time, started as ThisIsLike by Dmitry Paranyushkin, with notable contributions from Alexis Jacomy and Matthieu Jacomy, Dor Garbash, and Oleg Yarin.
TLDR
Infranodus encourages ecological thinking by promoting cognitive variability and network heuristics to help users avoid totalitarian thinking. The tool enables generating new ideas, visualizing text network graphs, and has an Insight Recommender System. InfraNodus is mobile and desktop compatible, SEO-optimized, and provides in-depth quantitative and qualitative insights for your content.
Developed by Nodus Labs, the tool has a rich history from ThisIsLike to InfraNodus, with notable contributions from several individuals including Alexis Jacomy, Matthieu Jacomy, Dor Garbash, and Oleg Yarin.
Company Overview
Infranodus is an innovative text network visualization tool that was developed by Nodus Labs with the purpose of promoting ecological thinking through cognitive variability and network heuristics. Infranodus provides tools, methodologies, and frameworks that enhance perception by making it adaptable and diverse, which ultimately helps avoid totalitarian thinking. The platform focuses on exploring the possibilities and unraveling the world in its polysingularity rather than seeking to disrupt existing systems.
To achieve this, Infranodus encourages exploration over efficiency, interest over productivity.
The company was established in 2007 by Dmitry Paranyushkin as ThisIsLike, a tool for associative thinking. The company has undergone a series of transformations that have culminated in the evolution of Infranodus as we know it today.
The tool's evolution has been facilitated through collaborations with Alexis Jacomy, Matthieu Jacomy, Dor Garbash, and Oleg Yarin. InfraNodus operates on NestJs Node.Js framework, Prisma, PostgreSQL, and Sigma Features Generate New Ideas Text Network Analysis and GPT-3 AI Infranodus uses text network analysis and OpenAI GPT-3 NLP AI tools to detect the structural gaps in your writing and generate interesting research questions that can help you generate new ideas.
All you need to do is to open the "Generate New Ideas" App and start writing. You can begin with a short 500-character statement, copy and paste your notes, a text that you've written, or a research abstract. Your text will be visualized as a graph and you will see the main keywords, the topics, and how they are related.
You will also be able to get some insights about the structure of your text, which may give you a different perspective on your writing. GPT-3 AI Natural Language Processing The built-in GPT-3 AI natural language processing AI will use the structural gaps detected in the graph to generate the research questions that bridge those gaps and help you generate new ideas that help you connect your thoughts in a new way. It uses state-of-the-art text-to-network conversion system to quickly build graphs that you can then use to reflect on and analyze your text itself.
Gradually the patterns emerge that show the most connected clusters of words together. Insight Recommender System InfraNodus has an Insight Recommender System that proposes an ecological thinking model.
This model helps you diversify your discourse when it's too focused on a single topic and stimulates you to focus your discourse when it's too dispersed. The system analyzes the existing connections between your ideas using the network graph. It then proposes new connections that you haven't thought of before using a combination of network science algorithms and GPT-3 AI.
Text Network Graph Visualization Text Network Conversion System InfraNodus' basic advantage is its state-of-the-art text-to-network conversion system. Every word is visualized as a node and every co-occurrence is the connection. You can use text to quickly build graphs which you can then use to reflect on and analyze the text itself.
Hashtags and Mentions To be more precise, you can use #hashtags and @mentions when you enter the information. Each #hashtag is a node and it connects to the other #hashtag, which is next to it.
You can also link the #hashtags to a specific node using the "mention" @ prefix. For example, #apples and #oranges are @fruits.
You can also import your data from any external text, a PDF file, Evernote, Twitter or Google, Gephi, and many other formats. Quantitative Insights with Analytics Panel Once you generate a text network graph, you can get insights both qualitatively, just looking at the graph, and quantitatively — using the Analytics panel.
The system will also provide you a recommendation as to what you can do next to your ideas or text: which topics you could develop, what's lacking, what you could do to the structure of your discourse. Usable Anywhere Mobile Accessibility InfraNodus is available on mobile phones and tablets, allowing users to easily access the tool from anywhere. Compatibility InfraNodus can work on your desktop or mobile device.
It runs on Neo4J graph database, Sigma.Js graph visualization library, Node.Js and uses Textexture text network analysis algorithm as well as Cytoscape, Graphology, and other open-source libraries. This makes it a versatile tool and helps ensure compatibility across platforms.
SEO-Optimized The text generated by InfraNodus can be optimized for search engines, enabling users to publish and share their work in a format that is easily discoverable by search engines. FAQ What is Infranodus? Infranodus is a text network visualization tool that provides tools, methodologies, and frameworks that enhance perception by making it adaptable and diverse, ultimately helping avoid totalitarian thinking.
The platform focuses on exploring the possibilities and unraveling the world in its polysingularity rather than seeking to disrupt existing systems. To achieve this, Infranodus encourages exploration over efficiency, interest over productivity.
Who is the team behind Infranodus? Infranodus was established in 2007 by Dmitry Paranyushkin as ThisIsLike, a tool for associative thinking. The tool's evolution has been facilitated through collaborations with Alexis Jacomy, Matthieu Jacomy, Dor Garbash, and Oleg Yarin.
InfraNodus operates on NestJs Node.Js framework, Prisma, PostgreSQL, and Sigma. What are the features of Infranodus?
Infranodus provides text network visualization tools that allow users to explore and extract meaningful insights from their data. These features include: Network analysis Topic modeling Visualization of keyword/phrase nodes Text summary generation Labeling of clusters with key concepts What is the main purpose of Infranodus? The primary objective of Infranodus is to promote ecological thinking through cognitive variability and network heuristics.
The tool aims to enhance perception by making it adaptable and diverse, ultimately helping users avoid totalitarian thinking. The platform focuses on exploring the possibilities and unraveling the world in its polysingularity rather than seeking to disrupt existing systems. How can Infranodus benefit businesses?
Infranodus can be beneficial to businesses in several ways, including: Extracting insights from large data sets with minimal effort/cost Identifying trends and patterns in data that might not be immediately apparent Summarizing text data to make it easier to analyze Providing networks and cluster analysis visualizations to explain connections between data points Analyzing communication patterns between staff or customers
