The new InfraNodus extension lets you analyze the content of any page on the web or Google search results to discover the blind spots in its content and generate new ideas and research questions using AI and network science.
Most mind mapping tools have a cold start problem, which can be overcome using a GPT3 AI text generator. We can then visualize the ideas as a text network and use the insights from graph theory to analyze the relations and reveal the main topics and structural gaps within
Music can be visualized as a graph. While there are many ways to do that, one of the possibilities is to represent the notes (and octaves) as the nodes and their co-occurrences as the connections between them. This representation allows us to reveal patterns in our composition and to use the powerful graph theory algorithms to uncover interesting insights about the structure of the music we’re playing.
Word clouds are used to generate visual summaries of text, however, they have one major deficiency: they completely lose track of the context. As a result, there is a risk of getting false meaningless results and making the wrong assumptions about the underlying data. Text network analysis and visualization can solve this problem.
Surveys usually come in CSV / Excel formats and network visualization techniques can be very helpful to make more sense of the data. In this short tutorial we will demonstrate how network analysis and visualization can be used to discover crucial relationships in the survey data.
