Graphs and Relational Learning
Read neighbouring pumps
BOTIE
Examples, experiments and workspace controls
Connect a tool's inputs to see what it produces. You can try a branch before connecting it to Result.
Try your own inputs
Use a copy of the example to try your own data. Keep the problem's assumptions in mind, and predict the effect of a change before checking the output. You can also add another branch to compare two ways of doing the calculation.
Context for this challenge
Gather neighbouring readings with matrix multiplication, then divide each sum by its degree, returning zero for degree zero.
Botie wants each pump to read nearby pumps' measurements. An isolated pump has no neighbour average to calculate.
Multiply adjacency by the reading vector, calculate row degrees, and divide sums by degrees with the declared zero fallback.
Examples and results
- Binary adjacency matrix
0 1 0 1 0 1 0 1 0 - Node readings
- 02
- 18
- 24
- Required result
- 08
- 13
- 28
- Binary adjacency matrix
0 1 1 0 - Node readings
- 01
- 15
- Required result
- 05
- 11
Try a fresh construction
This clears this draft, including its undo history. Earlier successful attempts stay recorded. Build again without opening hints to record an independent attempt.
Stuck? Inspect a worked construction
The tools below form one possible solution. You can assemble it, follow its signals, and undo that change to return to your own construction.
Assumptions and test precision
The zero fallback is explicit. For directed graphs, choose message orientation before multiplying. Numerical tests allow a difference of 0.001. Lists and structures must also have the right shape.
The hub averages three neighbours
This check uses the pieces from “Gather and average neighbouring readings”.
BOTIE
- Binary adjacency matrix
0 1 1 1 1 0 0 0 1 0 0 0 1 0 0 0 - Node readings
- 010
- 12
- 24
- 39
Use a number, text, or an array matching the requested result. For a matrix, put each row in brackets. Saving your prediction runs your current construction on these inputs and checks the earlier examples too.
Build a construction in the workspace above first. This case will test those pieces.
What metadata would help a later model distinguish an isolated pump from neighbours whose average really is zero?
Make missing-neighbour conventions explicit when carrying aggregated features forward.
The zero fallback is explicit. For directed graphs, choose message orientation before multiplying.
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