{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Start with one repeated product task that has a measurable failure or cost.",
    "change": "Read the methods and identify one change you can isolate in that workflow.",
    "baseline": "Your current implementation on the same held-out tasks.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Successful outcomes, response time, human rework, and total cost per successful task.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2610.25997",
  "title": "Federated Community Detection in Bipartite Networks",
  "summary": "FLICKER replaces centralized spectral decomposition of a horizontally partitioned matrix with federated power iteration that exchanges only matrix-vector products on local blocks, and BiFLICKER applies this to bipartite community detection. The cost is iterative cross-server communication rounds, a recursive grid search for spectral targets, and reliance on a reference server whose rescaled leading singular values must be valid global targets. It can fail when local sample sizes are too small for the reference server to estimate targets, when the data do not follow the assumed sparse bipartite stochastic block model, or when the horizontal partitioning assumption does not match the deployment.",
  "rationale": "This is federated graph analytics under privacy constraints, not LLM training, inference, or agent infrastructure, so it falls outside the production decisions an AI/ML engineer with a single GPU faces unless their product specifically requires privacy-preserving clustering of distributed graph data.",
  "technique": "Federated spectral clustering for bipartite graphs",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-10-19",
  "as_of": "2026-10-20",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.25997",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "FLICKER replaces centralized spectral decomposition of a horizontally partitioned matrix with federated power iteration that exchanges only matrix-vector products on local blocks, and BiFLICKER applies this to bipartite community detection. The cost is iterative cross-server communication rounds, a recursive grid search for spectral targets, and reliance on a reference server whose rescaled leading singular values must be valid global targets. It can fail when local sample sizes are too small for the reference server to estimate targets, when the data do not follow the assumed sparse bipartite stochastic block model, or when the horizontal partitioning assumption does not match the deployment.",
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      "result": "",
      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2610.25997",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Sequential BiFLICKER attains an overall ARI of 0.94 against centralized clustering on a LastFM user-artist network partitioned by country, and remains close to centralized spectral clustering in simulations.",
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    }
  ],
  "exposure_map": [
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      "dimension": "compute",
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    },
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "source_page": null,
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    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
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}