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… detector)
First full integration of the Graph detection pipeline: combines
largestEigenvalue (spectral growth) + labelPropagation (community
partition) + modularityScore (partition evaluation) into a single
scalar risk score.
Surface:
Graph.coordinationRiskScore
(alpha: double) (beta: double)
(eigenTol: double) (eigenIter: int) (lpIter: int)
(baseline: Graph<'N>) (attacked: Graph<'N>)
: double option
Composite formula (MVP):
risk = alpha * Δλ₁_rel + beta * ΔQ
where:
- Δλ₁_rel = (λ₁(attacked) - λ₁(baseline)) / max(λ₁(baseline), eps)
- ΔQ = Q(attacked, LP(attacked)) - Q(baseline, LP(baseline))
Both signals fire when a dense subgraph is injected: λ₁ grows
because the cartel adjacency has high leading eigenvalue; Q grows
because LP finds the cartel as its own community and Newman Q
evaluates that partition highly.
Weight defaults per Amara 17th-ferry initial priors:
- alpha = 0.5 spectral growth
- beta = 0.5 modularity shift
Tests (3 new, 34 total in GraphTests, all passing):
- Empty graphs -> None
- Cartel injection -> composite > 1.0 (both signals fire)
- attacked == baseline -> composite near 0 (|score| < 0.2)
Calibration deferred (Amara Otto-132 Part 2 correction #4 — robust
statistics via median + MAD): this MVP uses raw linear weighting
over differences. Full CoordinationRiskScore with robust z-scores
over baseline null-distribution is a future graduation once
baseline-calibration machinery ships. RobustStats.robustAggregate
(PR #295) already provides the median-MAD machinery; just needs a
calibration harness to use it.
14th graduation under Otto-105 cadence. First full integration
ship using 4 Graph primitives composed together (λ₁ + LP +
modularity + composer).
Build: 0 Warning / 0 Error.
Provenance:
- Concept: Aaron (firefly network + trivial-cartel-detect) +
Amara's composite-score formulations across 12th/13th/14th/
17th ferries
- Implementation: Otto (14th graduation)
Composes with:
- Graph.largestEigenvalue (PR #321)
- Graph.labelPropagation (PR #326)
- Graph.modularityScore (PR #324)
- RobustStats.robustAggregate (PR #295) — for future robust
variant
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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Pull request overview
Adds a new composite cartel/coordination detector to Zeta.Core.Graph by combining spectral growth (Δλ₁) and modularity shift (ΔQ) into a single coordinationRiskScore, along with new integration-style tests to validate expected behavior on empty input, injected-cartel structure, and baseline==attacked.
Changes:
- Added
Graph.coordinationRiskScorecomposite risk score combininglargestEigenvalue,labelPropagation, andmodularityScore. - Added F# tests covering empty-input (
None), cartel injection (score > 1.0), and baseline equality (near-zero score).
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 3 comments.
| File | Description |
|---|---|
| src/Core/Graph.fs | Introduces coordinationRiskScore and accompanying XML-doc comment describing the composite formula and calibration notes. |
| tests/Tests.FSharp/Algebra/Graph.Tests.fs | Adds three new tests exercising the composite score on empty graphs, injected clique/cartel structure, and baseline==attacked. |
Comment on lines
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| /// **Calibration note (per Amara Otto-132 Part 2 | ||
| /// correction #4 — robust statistics):** this MVP uses | ||
| /// simple linear weighting over raw differences. A full | ||
| /// `CoordinationRiskScore` (per Amara's 17th-ferry | ||
| /// corrected composite) uses robust z-scores | ||
| /// `(x - median(baseline)) / (1.4826 * MAD(baseline))` over | ||
| /// each metric, combined with tunable weights. That version | ||
| /// is a future graduation once baseline-null-distribution | ||
| /// calibration machinery ships. |
Comment on lines
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| let qBaseline = | ||
| modularityScore partitionBaseline baseline | ||
| |> Option.defaultValue 0.0 | ||
| let qAttacked = | ||
| modularityScore partitionAttacked attacked | ||
| |> Option.defaultValue 0.0 | ||
| let eps = 1e-12 | ||
| let spectralGrowth = (la - lb) / (max lb eps) | ||
| let modularityShift = qAttacked - qBaseline | ||
| Some (alpha * spectralGrowth + beta * modularityShift) |
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| /// Provenance: 12th + 13th + 14th + 17th-ferry composite | ||
| /// score formulations. Otto's 14th graduation — first | ||
| /// full integration ship using four Graph primitives | ||
| /// (`largestEigenvalue` + `labelPropagation` + | ||
| /// `modularityScore` + this composer). | ||
| let coordinationRiskScore |
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Apr 24, 2026
…ns tracked; 3 already shipped) (#330) * ferry: Amara 17th absorb — Cartel-Lab Implementation Closure + 5.5 Verification (8 corrections tracked) Two-part ferry: Amara's deep-research Implementation Closure for Cartel-Lab + her own GPT-5.5 Thinking verification pass with 8 load-bearing corrections. Otto correction-pass status (all 8 tracked): 1. λ₁(K₃) = 2 — ALREADY CORRECT PR #321 Otto-127 (independent convergence before verification arrived) 2. Modularity relational-not-absolute — ALREADY CORRECT PR #324 Otto-128 (caught mid-tick via hand-calc) 3. Cohesion/Exclusivity/Conductance replace entropy-collapse — SHIPPED PR #329 Otto-135 (3 primitives + 6 tests) 4. Windowed stake covariance acceleration — FUTURE GRADUATION 5. Event-stream → phase pipeline for PLV — FUTURE GRADUATION 6. 'ZSet invertible' → 'deltas support retractions' — ADR ALREADY PHRASED CORRECTLY (PR #316 never claimed full invertibility) 7. KSK 'contract' → 'policy layer' — FILED BACKLOG PR #318 Otto-124 (Max coord pending) 8. SOTA humility — DOC PHRASING (applied in new absorb docs) Amara's proposed 3-PR split NOT adopted (Otto-105 small- graduation cadence; content delivered across 7 ticks instead: PRs #317, #321, #323, #324, #326, #328, #329). Amara's proposed /cartel-lab/ folder NOT adopted (Otto-108 Conway's-Law: single-module-tree until interfaces harden). Current Graph.fs + test-support split works. Aaron's SharderInfoTheoreticTests flake flag (trailing Otto-132 note) filed as BACKLOG PR #327 Otto-133 — unrelated hygiene item. Amara's Otto-136 follow-up note: '#323 conceptually accepted, do not canonicalize until sharder test is seed-locked/ recalibrated'. Acknowledged — #323 lives in tests/Simulation/ already (test-scoped); 'canonicalize' = future promotion to src/Core/NetworkIntegrity/ per Amara's PR #3 split suggestion; that's gated on #327 completion. §33 archive header compliance. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> * lint: fix line-start PR-number header false-positive in 17th-ferry absorb --------- Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
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…18th graduation (Amara #4 robust) Two ships consolidated per the 'parallel PRs hit positional conflicts on tail-append' lesson: 1. RobustStats.robustZScore (baseline: double seq) -> (measurement: double) -> double option Returns (measurement - median) / (1.4826 · MAD). The 1.4826 constant scales MAD to be consistent with Gaussian stddev. MadFloor prevents blow-up when every baseline value equal. 2. Graph.coordinationRiskScoreRobust alpha beta eigenTol eigenIter lpIter (baselineLambdas: double seq) (baselineQs: double seq) (attacked: Graph<'N>) -> double option Upgrades coordinationRiskScore (PR #328) from raw linear differences to robust-standardized z-scores per Amara 17th-ferry correction #4. Caller provides baseline metric distributions; Z-scores calibrate thresholds from data. Why robust z-scores: adversarial data isn't normally distributed. An attacker can poison a ~normal distribution by adding a few outliers that inflate stddev, making subsequent real attacks look 'within one sigma'. Median+MAD survives ~50% adversarial outliers. Standard move in robust statistics literature; Amara's correction puts it on the Zeta composite. Tests (5 new; total 39 since main hasn't merged #331/#332 yet): - robustZScore None on empty baseline - robustZScore of measurement = median is 0 - robustZScore scales MAD by 1.4826 for Gaussian consistency (measurement 4 on baseline [1..5] ≈ 0.674) - coordinationRiskScoreRobust fires strongly on K4-injected graph given 5 baseline samples - coordinationRiskScoreRobust returns None on empty baselines BACKLOG rows added this tick per Aaron Otto-139 directives: 1. Signal-processing primitives (FFT + Hilbert) — unblocks Amara correction #5 Option B; Aaron standing-approval 2. F# DSL for entry points + graph-query-language standards compliance (Cypher / GQL / Gremlin / SPARQL / Datalog) 3. LINQ-compatible entry points for C# consumers — pair with F# DSL; two frontends, one algebraic backend 6 of 8 Amara 17th-ferry corrections now shipped or confirmed: Remaining: #6 ADR phrasing (already fine); #7 KSK naming (BACKLOG #318 Max coord pending); #8 SOTA humility (doc-phrasing discipline ongoing). Build: 0 Warning / 0 Error. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
AceHack
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…mara #4 robust) + 3 BACKLOG rows (#333) * core: RobustStats.robustZScore + Graph.coordinationRiskScoreRobust — 18th graduation (Amara #4 robust) Two ships consolidated per the 'parallel PRs hit positional conflicts on tail-append' lesson: 1. RobustStats.robustZScore (baseline: double seq) -> (measurement: double) -> double option Returns (measurement - median) / (1.4826 · MAD). The 1.4826 constant scales MAD to be consistent with Gaussian stddev. MadFloor prevents blow-up when every baseline value equal. 2. Graph.coordinationRiskScoreRobust alpha beta eigenTol eigenIter lpIter (baselineLambdas: double seq) (baselineQs: double seq) (attacked: Graph<'N>) -> double option Upgrades coordinationRiskScore (PR #328) from raw linear differences to robust-standardized z-scores per Amara 17th-ferry correction #4. Caller provides baseline metric distributions; Z-scores calibrate thresholds from data. Why robust z-scores: adversarial data isn't normally distributed. An attacker can poison a ~normal distribution by adding a few outliers that inflate stddev, making subsequent real attacks look 'within one sigma'. Median+MAD survives ~50% adversarial outliers. Standard move in robust statistics literature; Amara's correction puts it on the Zeta composite. Tests (5 new; total 39 since main hasn't merged #331/#332 yet): - robustZScore None on empty baseline - robustZScore of measurement = median is 0 - robustZScore scales MAD by 1.4826 for Gaussian consistency (measurement 4 on baseline [1..5] ≈ 0.674) - coordinationRiskScoreRobust fires strongly on K4-injected graph given 5 baseline samples - coordinationRiskScoreRobust returns None on empty baselines BACKLOG rows added this tick per Aaron Otto-139 directives: 1. Signal-processing primitives (FFT + Hilbert) — unblocks Amara correction #5 Option B; Aaron standing-approval 2. F# DSL for entry points + graph-query-language standards compliance (Cypher / GQL / Gremlin / SPARQL / Datalog) 3. LINQ-compatible entry points for C# consumers — pair with F# DSL; two frontends, one algebraic backend 6 of 8 Amara 17th-ferry corrections now shipped or confirmed: Remaining: #6 ADR phrasing (already fine); #7 KSK naming (BACKLOG #318 Max coord pending); #8 SOTA humility (doc-phrasing discipline ongoing). Build: 0 Warning / 0 Error. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> * fix(#333): 4 review-thread P1/P2s on robustZScore + coordinationRiskScoreRobust Active PR-resolve-loop on #333. 1. Doc/impl contradiction on MAD=0 (thread 59VhYb, P1): RobustStats.robustZScore doc said "returns None when MAD(baseline)=0" but impl uses MadFloor and returns Some finite value. Rewrote doc to match impl: explicit "MadFloor substituted when MAD collapses to zero" — floor reflects "scale is below epsilon" not "undefined." Implementation is the contract. 2. Multi-enumeration of baseline seq (thread 59VhYq, P1): robustZScore previously passed `baseline` to both `median` + `mad` which each call `Seq.toArray`. Expensive AND inconsistent for lazy/non-repeatable sequences (different values between enumerations = undefined behavior). Fixed: `Seq.toArray` once at entry, pass the materialized array to both. O(n) instead of O(2n); stable across lazy sources. 3. Name attribution in Graph.fs doc comment (thread 59VhY5, P1): "Amara 17th-ferry... Otto 18th graduation" → "external AI collaborator's 17th courier ferry... Eighteenth graduation under the Otto-105 cadence." Role-reference convention per AGENT-BEST-PRACTICES code/doc rule. 4. Array-vs-seq terminology (thread 59VhZG, P2): Graph.fs doc said callers "provide arrays" but the API is `double seq`. Rewrote: sequences + noted the materialize-once optimization in robustZScore so callers can pass any seq form without re-enumeration cost. Thread 59VhX9 (P3-label-in-P2-section mismatch) — already resolved on main via PR #341 which landed the signal- processing row correctly labeled "P2 research-grade." No fix needed on this branch. Build: 0 Warning(s) / 0 Error(s). 53 RobustStats + Graph tests pass. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.7 <noreply@anthropic.com>
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First full integration ship: combines largestEigenvalue + labelPropagation + modularityScore into a single risk score.
Cartel-injection test: score > 1.0 (both signals fire). Baseline-vs-baseline near-zero. Empty → None.
34 GraphTests passing. Robust-z-score variant deferred to future graduation when baseline-null-calibration harness ships.