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license: other
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# DBbun Crypto Synthetic Dataset

**DBbun Crypto Synthetic** is a large-scale, privacy-safe simulation of blockchain-style transactions.  

The dataset was **inspired by the paper**:
*“Beyond Static Datasets: A Behavior-Driven Entity-Specific Simulation to Overcome Data Scarcity and Train Effective Crypto Anti-Money Laundering Models.”* 

No data were copied or extracted from that study. Instead, DBbun recreated its behavioral logic and structural principles to generate fully artificial yet statistically realistic records.

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## Summary of the Generated Dataset

| Table | Approx. Rows | Description |
|--------|---------------|-------------|
| **transactions.csv** | ~500,000 | One synthetic transaction per row |
| **edges.csv** | ~10–15 million | Sender → receiver edges forming the transaction graph |
| **accounts.csv** | ~100,000 | Unique wallets and entities |
| **labels_entities.csv** | ~100,000 | Entity-level labels (`licit / illicit` + K-hop proximity) |
| **labels_transactions.csv** | ~500,000 | Transaction-level labels (`benign / suspicious` + heuristics) |
| **stats.json** | — | Summary counts and distributions |

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## Files Included

| File | Description | Rows (approx.) |
|------|--------------|----------------|
| `transactions.csv` | One row per synthetic transaction with timestamp, pattern, and fee. | 500K |
| `edges.csv` | Directed sender → receiver edges forming the transaction graph. | 10–15M |
| `accounts.csv` | Wallet-level entities with balances, lifetimes, and address counts. | 100K |
| `labels_entities.csv` | Entity labels (`licit / illicit`) + graph proximity (K-hop). | 100K |
| `labels_transactions.csv` | Transaction labels (`benign / suspicious`) + heuristic flags. | 500K |
| `stats.json` | Summary statistics and distributions. | — |

Each table is self-contained and can be joined using common keys such as `tx_id` (for transaction-level joins) and `account_id` (for entity-level joins).

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## Schema Overview

### Transactions
Each row represents a synthetic transaction in a blockchain-style ledger.

| Column | Description |
|--------|--------------|
| `tx_id` | Unique transaction identifier |
| `timestamp` | UTC datetime |
| `pattern` | Transaction behavior type (`regular`, `mixer`, `coinjoin`, `exchange_withdraw`, `fan_out`, `peel_chain`, `single_use`) |
| `num_inputs` | Number of input addresses |
| `num_outputs` | Number of output addresses |
| `total_in` | Total input amount |
| `total_out` | Total output amount |
| `fee` | Transaction fee |
| `tx_hash` | SHA-256 hash (deterministic and reproducible) |

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### Edges
Directed sender → receiver relationships that form the transaction graph.

| Column | Description |
|--------|--------------|
| `tx_id` | Transaction identifier |
| `timestamp` | UTC datetime |
| `sender` | Sending wallet or address |
| `receiver` | Receiving wallet or address |
| `value` | Amount transferred |
| `pattern` | Transaction behavior pattern |

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### Accounts
Wallet-level entity table representing participants in the system.

| Column | Description |
|--------|--------------|
| `account_id` | Unique wallet identifier |
| `entity_type` | Category (e.g., `exchange`, `mixer`, `mule`, `business`, `service`, `licit`, `nested`) |
| `first_seen` | Earliest activity timestamp |
| `last_seen` | Most recent activity timestamp |
| `current_balance` | Remaining balance |
| `n_addresses` | Number of associated addresses |

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### Labels — Entities
Entity-level labels describing risk, type, and graph proximity.

| Column | Description |
|--------|--------------|
| `account_id` | Wallet identifier |
| `entity_type` | Same as in accounts |
| `entity_label` | `licit` or `illicit` |
| `k_hop_dist` | Integer distance from an illicit entity |
| `k_hop_label` | Categorical proximity flag (`within_2hop`, `far`) |

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### Labels — Transactions
Labels and heuristics associated with each transaction.

| Column | Description |
|--------|--------------|
| `tx_id` | Transaction identifier |
| `pattern` | Behavioral pattern |
| `tx_label` | `benign` or `suspicious` |
| `is_fan_in` | True if many inputs converge |
| `is_fan_out` | True if one input splits into many |
| `is_roundish` | True if rounded amounts observed |
| `is_bursty_hour` | True if occurs during a local burst hour |
| `is_bursty_day` | True if part of a burst day |

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## Use Cases

- **Graph Analytics** — explore transaction flows, community detection, and centrality.  
- **Machine Learning** — train models for illicit-activity or suspicious-transaction detection.  
- **Education** — teach blockchain analytics and anti-money-laundering frameworks.  
- **Benchmarking** — stress-test ETL, graph databases, and networ