Link Predictions
The PREDICTIONS section of a node's detail view answers one precise question:
Which two concepts have never appeared in the same note, yet look — from the shape of the whole network — like they should be connected?
This layer is not AI: no network calls, no API key, works offline.
The Intuition: Rarer Shared Concepts Carry More Signal
Suppose A and B never co-occur, but both relate to C. How much C counts depends on how common C is:
- C is something like "design thinking", appearing everywhere → almost no information, since it touches everything
- C appears in only three notes → strong signal, because two things landing on the same obscure concept is unlikely to be coincidence
So a shared concept you lean on constantly counts for little, while a rare one you reached from two different directions counts for a lot.
Reading a Prediction
Each one carries three things:
| Element | Meaning |
|---|---|
| The candidate node | The concept proposed as a connection |
| Confidence | Strong, Moderate or Weak — how much the shared concepts add up to |
| Shared neighbours | The intermediate concepts behind the judgement, ordered rarest first — the top one weighed most |
That last column is what makes it explainable: you need not trust a rating, you can read "so A and B both relate to these three concepts" and judge for yourself.
Only pairs that never co-occurred
Pairs that have co-occurred never appear here — those are facts, and they belong to the matrix. Prediction deals strictly with what has not happened yet.
Where It Falls Short
- Too few notes produces almost nothing: not enough shared concepts to work from
- A universal node (one you linked from every single note) dilutes the signal — though a concept that common already counts for very little
- Nodes with zero references are skipped
Versus Semantic Relations
Both hunt for connections you have not made, on entirely different evidence:
| Based on | Finds | |
|---|---|---|
| Link prediction | Your link structure | Concepts worded differently but sitting in similar positions in the network |
| Semantic relations | The meaning of the text | Notes saying the same thing that you never connected |
One reads structure, the other meaning. Both are free, and they corroborate each other — a pair flagged by both is worth connecting first.
Next Steps
- Semantic Relations — Relatedness by meaning
- Co-occurrence & Matrix — Relationships that already exist
- Node Types — Badges read from the same network