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| "name": "BillSim", |
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| "http://mlcommons.org/croissant/1.1", |
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| "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": "Software Collection", |
| "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.", |
| "rai:hasSyntheticData": true, |
| "prov:wasDerivedFrom": [ |
| { |
| "@type": "prov:Entity", |
| "prov:label": "NumPy random seed (default: 42)", |
| "description": "BillSim is fully synthetic and has no real-world parent dataset. The 'derived-from' entity for synthetic data is the random seed plus the parameterized statistical distributions defined in billsim/config.py. The default release uses NumPy random seed 42 with injection_rate 0.05; the full benchmark protocol sweeps 10 seeds (42, 123, 456, 789, 1024, 2048, 3072, 4096, 5120, 6144) and 4 rates (0.01, 0.03, 0.05, 0.10). Distributions: Pareto (revenue), log-normal (usage volumes), Poisson (arrivals), uniform (pricing). No real-world data was sampled, scraped, purchased, or inherited." |
| } |
| ], |
| "prov:wasGeneratedBy": [ |
| { |
| "@type": "prov:Activity", |
| "prov:type": "Data Collection (synthetic generation)", |
| "prov:label": "Heterogeneous billing-graph synthesis", |
| "description": "A deterministic Python pipeline (billsim.generate) instantiates a 24-month simulated horizon of an enterprise usage-based-billing operation, producing 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). Generation is bit-level reproducible given (seed, injection_rate). No real customer, account, transaction, or other financial data is collected, sampled, or scraped at any stage.", |
| "prov:wasAttributedTo": [ |
| { |
| "@type": "prov:SoftwareAgent", |
| "@id": "billsim-generator", |
| "prov:label": "BillSim deterministic generator (billsim.generate)", |
| "description": "Python 3.9+ pipeline using NumPy and PyTorch Geometric. Source code released alongside the dataset under Apache-2.0." |
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| ] |
| }, |
| { |
| "@type": "prov:Activity", |
| "prov:type": "Data Annotation (programmatic labeling)", |
| "prov:label": "Anomaly injection and label generation", |
| "description": "Anomaly labels are generated programmatically at injection time, not annotated by humans. The injector (billsim.anomalies.injector) marks each affected node with a binary anomaly indicator (y in {0, 1}) and an integer anomaly type (anomaly_type in {0..6}). 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). Every node has at most one anomaly type, so multi-class labels are mutually exclusive. Anomaly placement is seeded for bit-level reproducibility given (seed, injection_rate). No human annotators participated.", |
| "prov:wasAttributedTo": [ |
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| "@type": "prov:SoftwareAgent", |
| "@id": "billsim-injector", |
| "prov:label": "BillSim deterministic anomaly injector (billsim.anomalies.injector)", |
| "description": "Python module that applies one of six parameterized anomaly templates to a configurable fraction of nodes per type." |
| } |
| ] |
| }, |
| { |
| "@type": "prov:Activity", |
| "prov:type": "Data Preprocessing", |
| "prov:label": "Feature normalization and train/test split", |
| "description": "Per-node-type z-score normalization (mean 0, std 1) on each numeric feature column. Categorical features (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 (months 1-18 train versus 19-24 test) over the 24-month horizon. No missing values, NaN, Inf, or out-of-bounds edge indices remain after preprocessing; an automated check verifies integrity on every regeneration.", |
| "prov:wasAttributedTo": [ |
| { |
| "@type": "prov:SoftwareAgent", |
| "@id": "billsim-preprocessor", |
| "prov:label": "BillSim preprocessor (billsim.graph.builder + billsim.config)", |
| "description": "Python modules invoked by the generator after graph synthesis and before tensor serialization." |
| } |
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| } |
| ], |
| "distribution": [ |
| { |
| "@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.", |
| "contentUrl": "data/billsim_default.pt", |
| "encodingFormat": "application/x-pytorch", |
| "md5": "1aff80c89fdf12d65b098f1a559de174" |
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| ], |
| "recordSet": [ |
| { |
| "@type": "cr:RecordSet", |
| "@id": "customer", |
| "name": "customer", |
| "description": "Customer entity records. 200 nodes. The full 11-node-type record set (customer, contract, sku, pricing_rule, usage_record, rated_charge, invoice, line_item, payment, ar_record, gl_entry) is enumerated in docs/datasheet.md and docs/croissant.json in the anonymous code mirror; this RecordSet is included as a representative example of the schema typing.", |
| "field": [ |
| { |
| "@type": "cr:Field", |
| "@id": "customer/segment", |
| "name": "segment", |
| "description": "Integer-encoded customer segment (business tier 0-3). Non-demographic.", |
| "dataType": "sc:Integer", |
| "source": { |
| "fileObject": {"@id": "billsim-default-pt"}, |
| "extract": {"column": "customer.x[:, 0]"} |
| } |
| }, |
| { |
| "@type": "cr:Field", |
| "@id": "customer/is_active", |
| "name": "is_active", |
| "description": "Boolean indicating whether the customer is currently active (1) or churned (0).", |
| "dataType": "sc:Boolean", |
| "source": { |
| "fileObject": {"@id": "billsim-default-pt"}, |
| "extract": {"column": "customer.x[:, 1]"} |
| } |
| }, |
| { |
| "@type": "cr:Field", |
| "@id": "customer/num_sub_accounts", |
| "name": "num_sub_accounts", |
| "description": "Count of sub-accounts associated with this customer.", |
| "dataType": "sc:Integer", |
| "source": { |
| "fileObject": {"@id": "billsim-default-pt"}, |
| "extract": {"column": "customer.x[:, 2]"} |
| } |
| } |
| ] |
| } |
| ] |
| } |
|
|
