Paper analysis: descriptive snapshot and simulation results

Supplementary analysis code, data, and generated figures for the paper on a decentralized, trust-aware nanopublication ecosystem.

Layout

analyze.py — Descriptive statistics over a Registry snapshot: trust-graph metrics (path depth, ratio mass, endorsement edges), identity counts, content concentration. Reads from data/, writes data/stats.json and figures/snapshot.svg.
fetch_snapshot.py — Pulls the inputs that analyze.py reads (per-account aggregates, agents, trust paths, event timestamps) from a public Registry endpoint, with caching under data/.
simulate_plots.py — Renders the simulation-study figure (figures/simulation-results.svg) from the per-phase numbers in network_simulation_results/Stats checkpoints.xlsx. The numbers are inlined in the script so it runs from a clean checkout.
compare_trustrank.py — Spearman rank correlation between IDEBT per-pubkey ratios and personalized PageRank seeded by the same setting; produces figures/trustrank_vs_idebt.pdf.

data/ — Cached snapshot inputs and the computed stats.json.
figures/ — Generated figures referenced by the paper.
network_simulation_results/ — Raw simulation outputs from the multi-node deployment study.

Snapshot figure:

python3 fetch_snapshot.py    # refresh data/ from the Registry endpoint
python3 analyze.py           # write data/stats.json and figures/snapshot.svg

Simulation figure:

python3 simulate_plots.py

Python 3.11+ with matplotlib, numpy, and requests.