+ part of dataset

README.md

@@ -1,3 +1,176 @@
+# 🕸 Ethereum Address Behavior Dataset — GNN + LSTM (Fraud Detection)
+
+This dataset is designed for **fraud detection on Ethereum addresses** using a **dual-modality approach**:
+- **Graph Neural Networks (GNN):** transaction graph structure.
+- **Recurrent Models (LSTM/Transformers):** time-series of address features.
+
+The dataset is built from:
+- **Ethereum public BigQuery dataset** (`bigquery-public-data.crypto_ethereum.transactions`).
+- **Etherscan labels + custom scam labels**.
+- **Balanced address list** of ~115k addresses (scam vs non-scam, contracts vs EOAs).
+
+## 📦 Dataset Collection Pipeline
+
+To reproduce or customize the dataset, use the instructions and code in the [eth-fraud-dataset-pipeline repository](https://github.com/fesevu/eth-fraud-dataset-pipeline).  
+That repository provides:
+- Scripts for downloading raw data from public sources (BigQuery, Etherscan, curated scam lists).
+- Code for merging, deduplicating, and balancing address labels.
+- Tools for building the GNN and LSTM datasets (parquet files, mappings, targets).
+- Utilities for generating checksums and manifests for data integrity.
+
+**You must run the provided scripts to generate the dataset locally; the data files are not stored in the GitHub repository.**
+
 ---
-license: cc-by-4.0
+
+## 📂 Repository Structure
+
+final/
+├─ gnn_dataset/ # GNN dataset (edges, meta, labels, mapping, targets)
+│ ├─ edges_all/edges.parquet
+│ ├─ edges_by_week/week=YYYY-Www/edges.parquet
+│ ├─ edges_by_month/month=YYYY-MM/edges.parquet
+│ ├─ meta/{week,month}_window_meta.parquet
+│ ├─ labels/targets_global.parquet
+│ ├─ mapping/address_id_map_labels.parquet
+│ ├─ targets/{week,month}_targets.parquet
+│ └─ README.md
+└─ lstm_dataset/ # LSTM dataset (daily → weekly → monthly aggregations)
+├─ daily_filtered.parquet
+├─ weekly.parquet
+├─ monthly.parquet
+└─ README.md
+
+- `gnn_dataset/` → GNN dataset (graph edges, slices, labels, mapping).
+- `lstm_dataset/` → LSTM dataset (tabular features, time-series).
+
+---
+
+## 🔑 Synchronization Between GNN and LSTM
+
+- Both use the same **address universe** (`node_id` mapping).
+- Both use the same **time windows**:
+  - ISO weeks (`YYYY-Www`) from `gnn_dataset/meta/week_window_meta.parquet`.
+  - Months (`YYYY-MM`) from `gnn_dataset/meta/month_window_meta.parquet`.
+
+---
+
+## 📂 Raw Data
+
+Alongside the processed datasets, we also provide the **raw parquet exports** (all parquet files are compressed with **Zstandard (zstd)**):
+
+final/
+├─ GNN/parquet/ # raw transaction parquet chunks for GNN
+│ ├─ transactions_daily_part-00000.parquet
+│ ├─ transactions_daily_part-00001.parquet
+│ └─ ...
+├─ LSTM/parquet/ # raw daily features parquet for LSTM
+│ ├─ daily_final_part-00000.parquet
+│ ├─ daily_final_part-00001.parquet
+│ └─ ...
+├─ addr_labels_balanced.csv # balanced address list with labels
+├─ addr_labels_balanced.csv # balanced subset with labels (used in GNN + LSTM)
+
+---
+
+### Contents
+- **`GNN/parquet/`** — raw transaction-level parquet files, containing:
+  - `from_address`, `to_address` (STRING, lowercase hex)  
+  - `block_number` (INT64)  
+  - `timestamp` (TIMESTAMP, UTC)  
+  - `value_wei`, `tx_fee_wei` (NUMERIC in source, stored as string later)  
+  - `nonce`, `input_data_size`, `contract_creation`, `tx_hash`, `day`
+- **`LSTM/parquet/`** — raw daily activity parquet files (address-day features before filtering).
+- **`addr_labels_big.csv`** — initial large list of Ethereum addresses (>1M), with scam/contract metadata, **not used directly** (later downsampled & balanced).
+- **`addr_labels_balanced.csv`** — final balanced list of ~115k addresses (scam vs non-scam, contract vs EOA), used for both **GNN** and **LSTM** datasets.
+
+All parquet files in this dataset are compressed using **Zstandard (zstd)** for efficient storage and fast access.
+
+These files are the **starting point** for the preparation scripts:
+- `build_unified_dataset.py` → creates `gnn_dataset/` (GNN).  
+- `build_lstm_dataset_lowmem.py` → creates `lstm_dataset/` (LSTM).  
+
+---
+
+## ⚖️ Labels
+
+- Source: Etherscan tags + curated scam lists.
+- Balanced across:
+  - **Scam vs Non-Scam**
+  - **Contract vs EOA**
+- Provided in:
+  - `gnn_dataset/labels/targets_global.parquet`
+  - `gnn_dataset/mapping/address_id_map_labels.parquet`
+
+---
+
+### Address Label Files
+
+Both `addr_labels_big.csv` (full set) and `addr_labels_balanced.csv` (balanced subset) share the same schema:
+
+| Field              | Type     | Units | Description |
+|--------------------|----------|-------|-------------|
+| address            | STRING   | hex   | Ethereum address (0x..., lowercase). |
+| is_scam            | INT64    | 0/1   | Scam label: 1 = scam, 0 = non-scam. |
+| description        | STRING   | —     | Free-text description (e.g. "Verified", "Phishing"). |
+| activity_start_ts  | TIMESTAMP| UTC   | First observed activity timestamp. |
+| activity_end_ts    | TIMESTAMP| UTC   | Last observed activity timestamp. |
+| is_contract        | INT64    | 0/1   | Address type: 1 = smart contract, 0 = EOA. |
+
+- **`addr_labels_big.csv`** — ~1M+ raw addresses with scam/contract metadata, **not used directly** (later downsampled and balanced).  
+- **`addr_labels_balanced.csv`** — final balanced subset (~115k addresses, scam vs non-scam, contract vs EOA), used in both **GNN** and **LSTM** datasets.  
+
+---
+
+## 📦 Use Cases
+
+- **Graph ML:** Train static embeddings (GraphSAGE, Node2Vec) or temporal GNNs.
+- **Sequence ML:** Train LSTM/Transformer on address time-series features.
+- **Fusion:** Combine GNN embeddings and LSTM features via `node_id`.
+- **Fraud detection:** Predict scam addresses, contracts vs EOAs.
+
+---
+
+## 🛠 Collection Details
+
+- Source: Ethereum mainnet via BigQuery.
+- Labels: from Etherscan + custom curated lists.
+- Timezone: UTC.
+- ETH amounts stored as Decimal(38,9), exported as strings for precision.
+- Data preparation optimized for BigQuery + Polars, fits in 12–24 GB RAM.
+
+## 🔒 Integrity
+- All files are checksummed (SHA256, optional MD5).
+- See `CHECKSUMS.md` for a human-readable table.
+- See `manifest.jsonl` for a machine-readable log (size, mtime, checksums).
+- To verify after download:
+  ```bash
+  python3 make_checksums.py --verify --base /path/to/final
+
+## 🗂 Source Datasets
+
+The address list and labels (scam/non-scam, description) were compiled from the following public datasets:
+
+- **Primary sources:**
+  - [xblock.pro Dataset #13](https://xblock.pro/#/dataset/13)
+  - [xblock.pro Dataset #25](https://xblock.pro/#/dataset/25)
+  - [xblock.pro Dataset #50](https://xblock.pro/#/dataset/50)
+  - [PTXPhish](https://github.com/blocksecteam/PTXPhish/tree/main?tab=readme-ov-file)
+  - [Phishing Contract Sigmetrics](https://github.com/blocksecteam/phishing_contract_sigmetrics25/tree/main)
+  - [Etherscan Open Source Contract Codes](https://etherscan.io/exportData?type=open-source-contract-codes)
+  - [MyEtherWallet Ethereum Lists](https://github.com/MyEtherWallet/ethereum-lists)
+  - [EtherScamDB](https://github.com/MrLuit/EtherScamDB/tree/master)
+  - [CryptoScamDB Blacklist](https://github.com/CryptoScamDB/blacklist)
+  - [ScamSniffer Scam Database](https://github.com/scamsniffer/scam-database)
+  - [Forta Network Labelled Datasets](https://github.com/forta-network/labelled-datasets)
+  - [Kaggle: Labelled Ethereum Addresses](https://www.kaggle.com/datasets/hamishhall/labelled-ethereum-addresses?select=eth_addresses.csv)
+  - [Etherscan Labels](https://github.com/brianleect/etherscan-labels/tree/main/data/etherscan/combined)
+  - [Kaggle: Ethereum Fraud Detection Dataset](https://www.kaggle.com/datasets/vagifa/ethereum-frauddetection-dataset/data)
+  - [Ethereum Fraud Datasets](https://github.com/surajsjain/ethereum-fraud-datasets/tree/main)
+  - [Kaggle: Ponzi Scheme Contracts](https://www.kaggle.com/datasets/polarwolf/ponzi-scheme-contracts-on-ethereum)
+  - [Ethereum Fraud Detection](https://github.com/eltontay/Ethereum-Fraud-Detection)
+
+- **Integration:**
+  - Addresses and labels from these sources were merged and deduplicated.
+  - The final balanced address list (~115k addresses) was constructed based on these datasets.
+
 ---

addr_labels_balanced.csv.zst

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addr_labels_big.csv.zst

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+size 100475757

lstm_dataset/README.md

@@ -0,0 +1,41 @@
+# 🕸 Ethereum Transaction Graph Dataset (GNN)
+
+This dataset represents **Ethereum transactions as edges** between addresses.  
+It is designed for Graph Neural Networks (GNN), both **static embeddings** and **temporal graph learning**.  
+The dataset contains 2-hop graphs, i.e., it includes neighbors of neighbors for each address.
+
+---
+
+## 📑 Contents
+
+- `edges_all/edges.parquet` — all transactions (full edge list).
+- `edges_by_week/week=YYYY-Www/edges.parquet` — weekly slices.
+- `edges_by_month/month=YYYY-MM/edges.parquet` — monthly slices.
+- `meta/{week,month}_window_meta.parquet` — time window ranges and statistics.
+- `labels/targets_global.parquet` — labeled addresses `(node_id, is_scam, is_contract, address)`.
+- `mapping/address_id_map_labels.parquet` — `(address, node_id)` mapping.
+- `targets/{week,month}_targets.parquet` — labeled nodes active in each window.
+
+---
+
+## 🔑 Edge Schema
+
+| Field            | Type   | Units    | Description |
+|------------------|--------|----------|-------------|
+| src_id           | UInt64 | —        | Source node ID (hash of lowercase Ethereum address). |
+| dst_id           | UInt64 | —        | Destination node ID (hash of lowercase Ethereum address). |
+| ts               | Int64  | seconds  | Unix timestamp of the transaction (UTC). |
+| value_wei        | STRING | wei      | Transaction value in wei (exact decimal stored as string). |
+| tx_fee_wei       | STRING | wei      | Transaction fee in wei (exact decimal stored as string). |
+| block_number     | Int64  | block    | Ethereum block number of the transaction. |
+| contract_creation| Bool   | —        | True if transaction created a smart contract. |
+| tx_hash          | STRING | hex      | Unique transaction hash. |
+
+---
+
+## Notes
+
+- Transactions are **not filtered**: all edges included.  
+- **Supervision**: loss computed only on labeled addresses.  
+- **Dynamic GNN**: use `edges_by_week/` or `edges_by_month/`.  
+- **Static embeddings**: use `edges_all/edges.parquet`.  

lstm_dataset/daily_filtered.parquet

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