Agent-based dashboard: how a deepfake spreads through a school network, and where a limited education budget actually helps. Illustrative, not validated.
Justin Visser
7f06ea0d6f
Preferential attachment via an attachment pool that holds one entry per edge endpoint, so uniform draws are degree-proportional: that is the whole hub-forming mechanism. A triangle step closes friend-of-a-friend links with probability triangleProb, giving friend-group clustering. Semantics ported from networkx, NOT its RNG stream (per the handoff, no cross-language number matching). Tests are property-based: size, edge bounds, connectivity, hub formation across seeds, plus exact determinism for a fixed seed. 'go test ./...', gofmt and golangci-lint all clean. |
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| cmd/spreadlab | ||
| internal/engine | ||
| .gitignore | ||
| go.mod | ||
| LICENSE | ||
| README.md | ||
spreadlab
Self-hosted dashboard that runs an agent-based deepfake-spread model live: change the levers, watch the spread, and (later) search for the best intervention under a budget. Output is illustrative, not validated.
Status: milestone 1, porting the simulation engine.
Layout:
internal/engine/- the pure simulation engine (no web dependencies)cmd/spreadlab/- the server binary (milestone 2)web/- Vue 3 + TypeScript frontend (milestone 2+)
MIT licensed.