What does it mean that gaps in your graph reveal what you need to learn?

Areas where connections should exist but do not indicate knowledge gaps.

Example: You map your understanding of machine learning and notice that 'gradient descent' connects to 'loss functions' and 'neural networks,' but 'regularization' sits as a near-orphan with a single weak link. You've read about regularization. You can define it. But your graph reveals the truth: you don't understand how it relates to overfitting, bias-variance tradeoff, or model selection. The gap between nodes isn't a formatting problem. It's a learning problem — and now you know exactly where to study next.

Try this: Pick a domain you consider yourself competent in — programming, cooking, investing, whatever you've spent real time on. Sketch 15-20 key concepts as nodes on paper or in a tool. Draw edges between every pair you can explain a specific relationship for. Now look at what's missing: which nodes have only one or two connections? Which pairs of nodes should be connected but aren't? Write down the three most surprising gaps. For each gap, write one sentence describing what you would need to learn to create that connection. You've just built a custom curriculum from the structure of your own knowledge.