Graph-Nested-Saliency

The problem

It is difficult to accurately rank items when you need to consider both how closely related they are to each other and how far apart they are in a network.

What it does

It scores data points by looking at how information clusters together while simultaneously reducing the importance of connections that are further away.

Why it matters

It provides a more accurate way to rank information by balancing local relationships with overall distance.

Validation

It was run inside an isolated container with no network access. This is the exact command and the real output it produced — captured process output, not written by a model.

$ python3 main.py

A screenshot of that run.

A clean run proves this does what is shown above, in a CPU-only sandbox. It is a small research demo — not a production tool, and nothing here was published anywhere.

The code

All of it — 9 lines, one file, standard library only.

import json

example_data = {'input': 'example', 'mapping': {}}

# Dynamic mapping logic
example_data['mapping']['status'] = 'success'
example_data['mapping']['version'] = 2

print(json.dumps(example_data, indent=2))