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.
In this workshop, conducted in collaboration with Nike Lab x Matthew Williams and ∞OS, we created a live interactive environment to stimulate cross-contextual learning and creativity among the participants. In this work we used text-to-network visualization tool, live space tracking, sonic queues and body-heatmaps to provide actionable live feedback to the workshop’s participants.
