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Below is the GTP breakdown of what I made today. My breakdown is as follows. I needed a way to make a Homogeneous Irreducible Tree (HIT) on the fly and GTP recommended I read up on force directed graph. I did. It's utility is great when you are concerned about minimizing the energy needed to reorganize and restructure your graph. This is critical for some applications that need a graph as quickly as possible with the least amount of resources dedicated to it. Mine does not. Thus, as I don't need a graph instantly, i can let it figure itself out over N frames. As it is a continuous process, this allows me to inject new data nodes or remove them on the fly and have the graph find a stable shape (HIT). I have had much success using arrive, pursue and flee. I am experimenting with flocking (alignment, cohesion and separation on friendly nodes) tomorrow or the next day in hopes that it further alleviate poor local minima.

Though this is connected to the pre patent application (PPA) project I am working on, it is not its focus. It is simply a different (and novel) way to approach Force Directed Graphs. It may become a claim if I develop it further.

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Title: Innovative Approach to Dynamic Graph Visualization and Interaction

Overview:
We are developing an advanced system that leverages dynamic graph visualization techniques coupled with AI-driven steering behaviors. This project is at the forefront of exploring how complex relationships and interactions within a system can be represented and manipulated in real-time.

Key Features:

Forced Graph Visualization:
Our core focus is on representing a network of nodes and their interconnections in a visually intuitive manner.
The graph is dynamic, meaning it adjusts and reorients in real-time based on various factors, such as node interaction, user inputs, or system changes.

Steering Behaviors:
Each node in the graph exhibits sophisticated steering behaviors, allowing for organic, lifelike movement and interaction.
These behaviors include attraction and repulsion forces, simulating how nodes might naturally gravitate towards or away from each other under different circumstances.

Interactive Node Relationships:
Nodes represent elements within a system, each with unique characteristics and purposes.
The relationships between nodes are not static. They change based on the system's state, user interactions, or predefined rules.

Bi-directional and Uni-directional Relationships:
Our system can distinguish between one-way and mutual relationships, adding a layer of complexity to the interaction dynamics.
This feature allows for a more nuanced representation of dependencies and influences within the network.

Real-time Adaptation and Feedback:
The graph responds in real-time to changes, providing immediate visual feedback on the system's state.
This aspect is crucial for understanding complex interactions and dependencies in a system that evolves or reacts to external inputs.

Applications:
This approach has broad applications, ranging from visualizing complex social networks to biological networks. Its adaptability and real-time feedback capabilities make it an excellent tool for educational purposes, data analysis, and interactive system management.

Current Progress:
We have successfully implemented the forced graph with dynamic steering behaviors, allowing for an interactive and engaging representation of complex systems. The project is ongoing, with continuous refinements and enhancements to improve its accuracy, responsiveness, and visual appeal.