Initial release for NeurIPS 2026 E&D submission

LICENSE

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+Creative Commons Attribution 4.0 International (CC BY 4.0)
+
+Copyright (c) 2026 Anonymous Authors
+
+This dataset and all generated data files in this directory are licensed under
+the Creative Commons Attribution 4.0 International License (CC BY 4.0).
+
+You are free to:
+
+  Share — copy and redistribute the material in any medium or format
+  Adapt — remix, transform, and build upon the material for any purpose,
+  even commercially.
+
+The licensor cannot revoke these freedoms as long as you follow the license
+terms.
+
+Under the following terms:
+
+  Attribution — You must give appropriate credit, provide a link to the
+  license, and indicate if changes were made. You may do so in any
+  reasonable manner, but not in any way that suggests the licensor
+  endorses you or your use.
+
+  No additional restrictions — You may not apply legal terms or
+  technological measures that legally restrict others from doing anything
+  the license permits.
+
+Notices:
+
+  You do not have to comply with the license for elements of the material
+  in the public domain or where your use is permitted by an applicable
+  exception or limitation.
+
+  No warranties are given. The license may not give you all of the
+  permissions necessary for your intended use. For example, other rights
+  such as publicity, privacy, or moral rights may limit how you use the
+  material.
+
+Full license text: https://creativecommons.org/licenses/by/4.0/legalcode
+Human-readable summary: https://creativecommons.org/licenses/by/4.0/
+
+To attribute this dataset, please cite:
+
+  Anonymous Authors. BillSim: A Synthetic Heterogeneous Graph Benchmark
+  for Non-Adversarial Anomaly Detection in Usage-Based Billing.
+  Submitted to NeurIPS 2026 Evaluations & Datasets Track, 2026.

README.md

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----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+task_categories:
+  - graph-ml
+tags:
+  - graph-anomaly-detection
+  - heterogeneous-graphs
+  - non-adversarial-anomalies
+  - billing-pipelines
+size_categories:
+  - 10K<n<100K
+---
+
+# BillSim — A Synthetic Heterogeneous Graph Benchmark for Non-Adversarial Anomaly Detection in Usage-Based Billing
+
+Anonymous Authors
+Submitted to NeurIPS 2026 Evaluations & Datasets Track
+2026
+
+## Overview
+
+BillSim is a fully synthetic heterogeneous graph benchmark for non-adversarial anomaly detection in usage-based billing pipelines. Existing graph anomaly detection benchmarks (Amazon reviews, Bitcoin OTC/Alpha, Yelp, UNSW-NB15, DGraph) model adversarial behaviour: fraud, spam, attacks, intentional concealment. BillSim targets the complementary regime that dominates enterprise systems-of-record: configuration errors, integration drift, and timing mismatches that are economically consequential (revenue leakage, customer overbilling) yet not adversarial.
+
+The headline finding the benchmark is built to expose: a 0.374 AUROC gap on peer-comparison anomalies between a hand-coded oracle (0.965) and the best GNN (0.591), localizing the limitation to message-passing aggregation rather than task ill-posedness. In abstract-level shorthand, this is the 0.37 AUROC oracle gap.
+
+## Dataset At A Glance
+
+- 63,155 nodes across 11 entity types
+- 108,930 edges across 14 relationship types
+- 6 anomaly types organized by graph reasoning pattern: structural / relational / peer-comparison
+- 5 baselines benchmarked: MLP, GCN, GAT, RGCN, HGT
+- 10 random seeds per configuration
+- 4 anomaly injection rates: 0.01, 0.03, 0.05, 0.10
+- 24 simulated billing months; temporal split for dynamic entities, deterministic 75/25 random split for static entities
+
+## Schema
+
+### Node Types (11)
+
+| Type | Count | Features |
+|------|-------|----------|
+| customer | 200 | segment, is_active, num_sub_accounts |
+| contract | 389 | duration_months, payment_terms, status, total_committed_value |
+| sku | 50 | category, base_price, is_active |
+| pricing_rule | 1,111 | rate_type, rate_value, is_current |
+| usage_record | 14,601 | volume, unit_cost |
+| rated_charge | 14,601 | amount |
+| invoice | 3,547 | total_amount, status |
+| line_item | 14,601 | amount |
+| payment | 3,547 | amount, method |
+| ar_record | 3,547 | status, amount |
+| gl_entry | 6,961 | abs_amount |
+| **Total** | **63,155** | |
+
+All feature vectors are z-score normalized per node type. Categorical features are integer-encoded. The `gl_entry` feature deliberately omits the sign of the GL amount to prevent revenue/cash leakage into the learning task.
+
+### Edge Types (14)
+
+| Edge | Source -> Target | Count |
+|------|------------------|-------|
+| subscribes_to | customer -> contract | 389 |
+| has_sku | contract -> sku | 1,111 |
+| governs | pricing_rule -> sku | 1,111 |
+| applies_to | pricing_rule -> contract | 1,111 |
+| generates_usage | customer -> usage_record | 14,601 |
+| for_sku | usage_record -> sku | 14,601 |
+| rates_to | usage_record -> rated_charge | 14,601 |
+| priced_by | rated_charge -> pricing_rule | 14,601 |
+| billed_on | rated_charge -> line_item | 14,601 |
+| part_of | line_item -> invoice | 14,601 |
+| invoiced_to | invoice -> customer | 3,547 |
+| settles | payment -> invoice | 3,547 |
+| opens_ar | invoice -> ar_record | 3,547 |
+| posts_to | invoice -> gl_entry | 6,961 |
+| **Total** | | **108,930** |
+
+The homogeneous view doubles edge count by adding reverse edges.
+
+## Anomaly Taxonomy
+
+Six anomaly types are organized into three graph-reasoning categories. The categorization is the analytical contribution of the benchmark: it makes the architectural success and failure modes of GNNs interpretable.
+
+| # | Name | Target Node | Category | Mechanism |
+|---|------|-------------|----------|-----------|
+| 1 | Missing Metering | customer | relational | Usage stops while contract remains active |
+| 2 | Orphaned Lines | line_item | relational | Invoice customer differs from charge customer (cross-path violation) |
+| 3 | Pricing Mismatch | rated_charge | structural | Charge amount inconsistent with volume x rate from connected pricing rule |
+| 4 | Stale Discount | pricing_rule | peer-comparison | Rate diverges from peer rules governing the same SKU |
+| 5 | Revenue Recognition | gl_entry | structural | Invoice missing the expected cash GL entry (sibling-count check) |
+| 6 | Premature AR | ar_record | structural | AR record closed despite incomplete payment sum |
+
+Labels are provided per node type as a binary `y` (0 = normal, 1 = anomalous) and as `anomaly_type` (0 = normal, 1-6 = the type above). Each node carries at most one anomaly label; no overlap between types.
+
+Injection signatures have been audited for feature leakage: per-type Cohen's d remains below 0.2, ensuring detection requires graph structure rather than feature shortcuts.
+
+## Headline Result
+
+At injection rate 0.05 across 10 seeds, the V4 result table is:
+
+| Type | MLP | GCN | GAT | RGCN | HGT | Oracle | Best GNN | Gap |
+|------|-----|-----|-----|------|-----|--------|----------|-----|
+| Missing Metering | 0.512 | 0.730 | 0.398 | 0.784 | **0.827** | 0.866 | HGT | +0.038 |
+| Pricing Mismatch | 0.684 | 0.571 | 0.867 | **0.980** | 0.979 | 0.881 | RGCN | -0.099 |
+| Orphaned Lines | 0.505 | 0.967 | 0.968 | 0.922 | **0.988** | 1.000 | HGT | +0.012 |
+| **Stale Discount** | 0.560 | 0.571 | 0.476 | **0.591** | 0.540 | **0.965** | RGCN | **+0.374** |
+| Revenue Recognition | 0.509 | 0.965 | 0.928 | 0.497 | **0.987** | 1.000 | HGT | +0.013 |
+| Premature AR | 0.509 | 0.519 | 0.533 | **0.996** | 0.991 | 1.000 | RGCN | +0.004 |
+
+The 0.374 oracle-GNN gap on Stale Discount is an order of magnitude larger than any other gap. Because the gap is exposed by a hand-coded peer-comparison oracle that achieves 0.965 AUROC, the limitation is established as architectural — message-passing aggregation cannot represent peer-distribution comparison — rather than the task being ill-posed.
+
+## Data Generation
+
+The dataset is fully synthetic. No real billing data was used at any stage. The generation pipeline is a configurable Python module with deterministic NumPy seeding. Default parameters:
+
+- Revenue distribution: lognormal contract sizes producing a Pareto-like tail
+- Usage volumes: log-normal (mu = 6.0, sigma = 1.2)
+- Customer arrivals: front-loaded declining Poisson
+- Contract durations: uniform 6-36 months
+- Pricing: uniform 0.01-10.0 base rate with +/-20% per-contract variation
+
+All parameters are documented in the configuration module of the released code.
+
+## Train / Test Split
+
+- Dynamic entities (usage_record, rated_charge, invoice, line_item, payment, ar_record, gl_entry): temporal split. Months 1-18 -> train, months 19-24 -> test.
+- Static entities (customer, contract, sku, pricing_rule): deterministic 75/25 random split keyed off `(seed + stable_offset(node_type))`. The stable offset is an MD5-derived integer per node type name, ensuring split reproducibility across seeds and node types.
+
+## Preprocessing
+
+- All features z-score normalized per column (mean 0, std 1)
+- Categorical features integer-encoded
+- No missing values; no NaN or Inf values (verified by automated integrity check)
+- No out-of-bounds edge indices (verified)
+
+## Intended Uses
+
+- Benchmarking GNN methods for non-adversarial anomaly detection on heterogeneous temporal graphs
+- Evaluating which graph reasoning patterns (structural sibling counting, multi-hop relational path matching, relation-specific traversal, peer-distribution comparison) different architectures can or cannot represent
+- Diagnostic studies of message-passing limitations, including ablations against hand-coded oracle ceilings
+- Research on architectural extensions that close the peer-comparison gap
+
+## Not-Intended Uses
+
+- Production billing system deployment. The synthetic distributions do not replicate the complexity of real billing data.
+- Training fraud detection models. The anomalies in BillSim are non-adversarial system errors, not adversarial attack patterns. Fraud involves intentional concealment and shifting tactics; billing errors do not.
+- Drawing conclusions about real-customer billing accuracy. The benchmark exists to evaluate model architectures, not to model any specific operator's billing platform.
+
+## Format
+
+PyTorch Geometric `HeteroData` objects serialized as `.pt` files, accompanied by Python generation scripts that reproduce the dataset deterministically from any seed and injection rate.
+
+## Licenses
+
+- Data: CC-BY-4.0
+- Code: Apache-2.0
+
+## Citation
+
+Anonymous Authors. BillSim: A Synthetic Heterogeneous Graph Benchmark for Non-Adversarial Anomaly Detection in Usage-Based Billing. Submitted to NeurIPS 2026 Evaluations & Datasets Track, 2026.

billsim_default.pt

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+oid sha256:a1b956386dbf7a73177e768657ae329e2e96496790a9c8ebe0421c5b5cbeb5a6
+size 4285402

croissant.json

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+{
+  "@context": {
+    "@language": "en",
+    "@vocab": "https://schema.org/",
+    "citeAs": "cr:citeAs",
+    "column": "cr:column",
+    "conformsTo": "dct:conformsTo",
+    "cr": "http://mlcommons.org/croissant/",
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+    "field": "cr:field",
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+    "fileSet": "cr:fileSet",
+    "format": "cr:format",
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+    "isLiveDataset": "cr:isLiveDataset",
+    "jsonPath": "cr:jsonPath",
+    "key": "cr:key",
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+    "parentField": "cr:parentField",
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+    "regex": "cr:regex",
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+    "replace": "cr:replace",
+    "samplingRate": "cr:samplingRate",
+    "sc": "https://schema.org/",
+    "separator": "cr:separator",
+    "source": "cr:source",
+    "subField": "cr:subField",
+    "transform": "cr:transform"
+  },
+  "@type": "sc:Dataset",
+  "name": "BillSim",
+  "conformsTo": "http://mlcommons.org/croissant/1.0",
+  "description": "BillSim is a synthetic heterogeneous graph benchmark for non-adversarial anomaly detection in usage-based billing pipelines. The default dataset contains 63,155 nodes across 11 entity types (customer, contract, sku, pricing_rule, usage_record, rated_charge, invoice, line_item, payment, ar_record, gl_entry) and 108,930 edges across 14 relationship types (subscribes_to, has_sku, governs, applies_to, generates_usage, for_sku, rates_to, priced_by, billed_on, part_of, invoiced_to, settles, opens_ar, posts_to). Six anomaly types are organized into three categories: structural (Pricing Mismatch, Revenue Recognition, Premature AR Closure), relational (Missing Metering, Orphaned Invoice Lines), and peer-comparison (Stale Discount). Five baselines are evaluated (MLP, GCN, GAT, RGCN, HGT) across 10 random seeds and 4 injection rates (0.01, 0.03, 0.05, 0.10). Headline finding: a 0.374 AUROC oracle gap on peer-comparison anomalies between a hand-coded oracle (0.965 AUROC on Stale Discount) and the best message-passing baseline (RGCN at 0.591; HGT at 0.540 is statistically indistinguishable), localizing the limitation to message-passing aggregation rather than task ill-posedness. Distributed as a PyTorch Geometric HeteroData (.pt) tensor plus deterministic Python generation scripts.",
+  "citeAs": "Anonymous Authors. BillSim: A Synthetic Heterogeneous Graph Benchmark for Non-Adversarial Anomaly Detection in Usage-Based Billing. Submitted to NeurIPS 2026 Evaluations & Datasets Track, 2026.",
+  "license": "https://creativecommons.org/licenses/by/4.0/",
+  "url": "[anonymized for double-blind review]",
+  "version": "4.0.0",
+  "datePublished": "2026-05-06",
+  "keywords": [
+    "graph anomaly detection",
+    "heterogeneous graphs",
+    "non-adversarial anomalies",
+    "billing pipelines",
+    "GNN benchmarks"
+  ],
+  "creator": {
+    "@type": "sc:Person",
+    "name": "Anonymous Authors",
+    "email": "anon@example.com"
+  },
+  "rai:dataCollection": "Fully synthetic dataset generated by a deterministic Python pipeline. No real customer, account, transaction, or other financial data was collected, sampled, scraped, or otherwise sourced from any real-world subject or system. Every node, edge, feature, and label is produced by parameterized statistical distributions (Pareto for revenue, log-normal for usage volumes, Poisson for arrivals, uniform for pricing) seeded with a NumPy random state for bit-level reproducibility.",
+  "rai:dataCollectionType": "Synthetic data generation",
+  "rai:dataPreprocessingProtocol": "Per-node-type z-score normalization (mean 0, std 1) on each numeric feature column. Categorical features (e.g., customer segment, payment terms, SKU category, payment method) are integer-encoded with stable per-process hashing. Train/test split is deterministic per seed: static entity types (customer, contract, pricing_rule, sku) use a 75/25 random split; dynamic entity types (usage_record, rated_charge, invoice, line_item, payment, ar_record, gl_entry) use a temporal split of months 1-18 (train) versus 19-24 (test) over a 24-month simulated horizon. No missing values, no NaN, no Inf, and no out-of-bounds edge indices are present; integrity is verified by an automated check on every regeneration.",
+  "rai:dataAnnotationProtocol": "Anomaly labels are generated programmatically at injection time, not annotated by humans. The injector marks each affected node with a binary anomaly indicator (y in {0, 1}) and an integer anomaly type (anomaly_type in {0, 1, 2, 3, 4, 5, 6}). Every node has at most one anomaly type assigned, so the multi-class label set is mutually exclusive across the six anomaly categories.",
+  "rai:dataUseCases": "Intended use: benchmarking graph neural network architectures on non-adversarial anomaly detection in heterogeneous temporal graphs, and evaluating which graph-reasoning patterns (structural degree counting, multi-hop path matching, relation-specific traversal, peer comparison) different architectures excel at. Not intended for fraud detection: anomalies in BillSim are operational and configuration errors, not adversarial attack patterns; fraud involves intentional concealment, billing errors do not. Not intended for production billing system deployment: the synthetic distributions deliberately do not replicate real billing data complexity.",
+  "rai:dataBiases": "BillSim is fully synthetic and contains no demographic, geographic, or otherwise protected-class attributes. The data generator does not encode race, gender, age, ethnicity, nationality, sexual orientation, religion, disability, or any proxy thereof. Customer entities have only segment (a non-demographic business tier index 0-3) and account-activity attributes. Statistical distributions used to sample synthetic feature values (Pareto revenue, log-normal usage, Poisson arrivals) are documented in billsim/config.py and are intended to reproduce the long-tailed shapes typical of usage-based-billing systems while introducing no demographic bias.",
+  "rai:personalSensitiveInformation": "None. All entity identifiers are sequential integers, all features are synthetic numeric or categorical encodings, and no field carries personal, financial, biometric, geolocation, or otherwise sensitive information about any real person or organization.",
+  "rai:dataSocialImpact": "Low. The dataset contains no real-world subjects and cannot be used to identify, target, or impact any individual or group. The primary intended impact is methodological: it provides a reproducible benchmark for studying when and why message-passing graph neural networks fail on operational anomaly-detection tasks that require peer-distribution comparison.",
+  "rai:dataLimitations": "BillSim covers six anomaly types representative of common operational failure modes in usage-based billing (missed metering, pricing mismatches, orphaned invoice lines, stale discounts, revenue recognition errors, premature AR closure) but is not exhaustive of real-world billing system failure modes. Synthetic distributions approximate but do not replicate the full complexity of production billing data (no tax tiers, no currency mismatches, no retroactive adjustments, no off-cycle events).",
+  "rai:dataReleaseMaintenancePlan": "The dataset is released as a single default PyTorch Geometric HeteroData artifact plus the deterministic generation pipeline. Because the generator is deterministic given (seed, injection_rate), the dataset can be regenerated indefinitely without depending on continued hosting of the binary artifact. Issues and contributions are accepted via the anonymous code mirror referenced in the paper.",
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+      "@type": "cr:FileObject",
+      "@id": "billsim-default-pt",
+      "name": "billsim_default.pt",
+      "description": "Default pre-generated dataset (seed=42, injection_rate=0.05). PyTorch Geometric HeteroData object with 11 node types, 14 edge types, z-score-normalized features, binary anomaly labels (y), integer anomaly type labels (anomaly_type in 0-6), and temporal train/test masks.",
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+      "field": [
+        {
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+          "@id": "customer/segment",
+          "name": "segment",
+          "description": "Integer-encoded customer segment (business tier 0-3). Non-demographic.",
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+          "description": "Count of sub-accounts associated with this customer.",
+          "dataType": "sc:Integer",
+          "source": {
+            "fileObject": {"@id": "billsim-default-pt"},
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+          }
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+      ]
+    }
+  ]
+}