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Datasets:
temporal-twins-benchmark
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temporal-twins

Tags:
temporal-graph-learning
fraud-detection
synthetic-data
benchmark
upi
causal-evaluation
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metadata 
license: apache-2.0
library_name: pytorch
tags:
  - temporal-graph-learning
  - fraud-detection
  - synthetic-data
  - benchmark
  - upi
  - causal-evaluation
  - matched-controls
  - sequence-modeling
  - dynamic-graph-learning
  - neurips
datasets:
  - temporal-twins-benchmark/temporal-twins

Temporal Twins Release Bundle

This release/ directory is a manual-hosting bundle for the Temporal Twins benchmark. It is intended to be uploaded later to a repository such as Hugging Face, Kaggle, Dataverse, OpenML, or another archival host. Hosting has not been performed by this preparation step.

What This Release Contains

  • DATASET_CARD.md: NeurIPS-style dataset card for the benchmark
  • README_REPO.md: copied repository README from the project root
  • LICENSE: copied project license file
  • results/: final deterministic paper-suite outputs
  • configs/: benchmark configuration files and a paper-suite reference config
  • metadata/: Croissant metadata and validation notes
  • data/: empty per-mode/per-seed directory scaffold plus generation instructions
  • MANIFEST.sha256: SHA256 manifest for all files in this bundle

How To Use The Data

  1. Read DATASET_CARD.md for benchmark scope, schema, intended use, and limitations.
  2. Read metadata/temporal_twins_croissant.json for machine-readable release metadata.
  3. Use results/paper_suite_summary.csv and results/paper_suite_summary.md for the paper-ready reference results.
  4. Populate data/ with the generated per-seed transaction and matched-prefix files before public hosting.

How To Regenerate The Data

The repository does not currently store per-seed transactions.parquet and matched_pairs.parquet release exports. To generate them without changing benchmark logic, follow the instructions in data/README_GENERATION.md.

How To Reproduce Paper Results

The final deterministic paper suite used:

  • benchmark groups: oracle_calib, easy, medium, hard
  • benchmark modes:
    • oracle_calib -> temporal_twins_oracle_calib
    • easy, medium, hard -> temporal_twins
  • seeds: 0 1 2 3 4
  • num_users = 350
  • simulation_days = 45
  • fast_mode = false
  • n_checkpoints = 8
  • device: cpu

Reference artifacts:

  • results/paper_suite_runs.csv
  • results/paper_suite_summary.csv
  • results/paper_suite_runtime.csv
  • results/paper_suite_failed_checks.csv
  • results/paper_suite_summary.md
  • results/paper_suite_meta.json

File Structure 

release/
├── README.md
├── README_REPO.md
├── DATASET_CARD.md
├── LICENSE
├── MANIFEST.sha256
├── RELEASE_CHECKLIST.md
├── data/
│   ├── README_GENERATION.md
│   ├── oracle_calib/seed_{0..4}/
│   ├── easy/seed_{0..4}/
│   ├── medium/seed_{0..4}/
│   └── hard/seed_{0..4}/
├── configs/
├── results/
└── metadata/

Hosted URLs

Licenses

  • Code: Apache License 2.0 (Apache-2.0)
  • Dataset and generated benchmark artifacts: Creative Commons Attribution 4.0 International (CC-BY-4.0)
  • Code SPDX-License-Identifier: Apache-2.0
  • Dataset SPDX-License-Identifier: CC-BY-4.0
  • No real UPI data or personal financial records are included in this release bundle.

Notes

  • This bundle contains no real UPI data, no real users, no real bank accounts, and no personal financial records.
  • The benchmark code, generator logic, labels, matched-prefix protocol, and model logic were not modified while preparing this release directory.