Datasets:

quanturong
/

EthPhishGraph-2026

---
license: cc-by-4.0
task_categories:
- graph-ml
- node-classification
tags:
- ethereum
- blockchain
- phishing-detection
- gnn
- graph-neural-network
- adversarial-robustness
- fraud-detection
- cryptocurrency
size_categories:
- 1K<n<10K
language:
- en
pretty_name: EthPhishGraph-2026
---
# EthPhishGraph-2026

Ego-graph K=2 dataset of Ethereum phishing accounts for Graph Neural Network based phishing detection and adversarial robustness research.

## 📊 Dataset Statistics

| | Count | % Phishing |
|---|---|---|
| **Total targets** | **7,036** | 50.0% |
| Phishing | 3,518 | - |
| Normal | 3,518 | - |

### Temporal split (60/20/20)

| Partition | Count | Phishing ratio | Period |
|---|---|---|---|
| Train | 4,221 | 56.8% | ≤ T1 = 2018-04-23 |
| Val | 1,407 | 37.3% | T1 → T2 = 2018-07-12 |
| Test | 1,408 | 42.2% | > T2 |

### Graph size

| Partition | Avg nodes | Avg edges |
|---|---|---|
| Train | 2,318 | 5,425 |
| Val | 2,482 | 5,376 |
| Test | 3,677 | 7,297 |

> hop-2 chiếm ~99% số node mỗi graph. Số cạnh (edges) tính cả 2 chiều trong hop-1/hop-2.

## 📁 Dataset Structure

```
EthPhishGraph-2026/
├── README.md                          # This file
├── LICENSE                            # CC-BY-4.0
├── ego_graph_k2_dataset.pkl           # 4.5 GB — Main pickle (train/val/test splits)
├── temporal_split.csv                 # 449 KB — Address → (label, partition, anchor_ts)
├── temporal_split_meta.json           # — Split metadata (T1, T2, counts)
├── per_target_neighbors.pkl           # 11 MB — Precomputed hop-1 neighbor lists
├── unique_hop1_neighbors.csv          # — Deduplicated neighbor addresses
├── feat_stats.pt                      # — Normalization stats (mean, std) from TRAIN only
├── bad_files.txt                      # — Known bad hop-2 files to skip
├── raw_txs/                           # Raw transaction JSONs (for Stage 6 attacks)
│   ├── phishing_hop1_txs.tar.gz       # ~50 MB — Phishing accounts' hop-1 txs
│   ├── normal_hop1_txs.tar.gz         # ~100 MB — Normal accounts' hop-1 txs
│   └── hop2_neighbor_txs.tar.gz       # ~5 GB — Hop-2 neighbor txs (compressed from 20 GB)
└── misc/                              # Optional artifacts (not needed for basic use)
    ├── stage1_outputs.tar.gz
    ├── stage4_imbalanced.tar.gz
    └── stage8_crosscheck_D2.tar.gz
```

## 🔬 Data Schema

### `ego_graph_k2_dataset.pkl` (main file)
```python
import pickle
with open("ego_graph_k2_dataset.pkl", "rb") as f:
    data = pickle.load(f)
# data = {"train": [...], "val": [...], "test": [...]}
```
Each entry is a `dict`:

| Key | Type | Shape | Description |
|---|---|---|---|
| `target` | `str` | — | Ethereum address `0x...` |
| `label` | `int` | — | 0 = normal, 1 = phishing |
| `target_idx` | `int` | — | Position of target in node list |
| `n_nodes` | `int` | — | Total nodes in the ego-graph |
| `n_edges` | `int` | — | Total directed edges |
| `n_hop1` | `int` | — | Number of hop-1 neighbors |
| `node_features` | `np.ndarray` | `[N, 11]` | See **Node features** below |
| `edge_index` | `np.ndarray` | `[2, E]` | COO format (source, destination) |
| `edge_attr` | `np.ndarray` | `[E, 4]` | See **Edge features** below |
| `cutoff` | `int` | — | Unix timestamp used as temporal cutoff |

#### Node features (11 columns)

| Col | Name | Description |
|---|---|---|
| 0 | `in_count` | Number of incoming transactions |
| 1 | `out_count` | Number of outgoing transactions |
| 2 | `in_value_sum` | Total incoming value (wei) |
| 3 | `out_value_sum` | Total outgoing value (wei) |
| 4 | `in_value_mean` | Mean incoming value (wei) |
| 5 | `out_value_mean` | Mean outgoing value (wei) |
| 6 | `first_ts` | First tx timestamp (⚠️ absolute — recommend to drop) |
| 7 | `last_ts` | Last tx timestamp (⚠️ absolute — recommend to drop) |
| 8 | `active_span` | `last_ts - first_ts` (relative, safe) |
| 9 | `unique_in_peers` | Number of distinct senders |
| 10 | `unique_out_peers` | Number of distinct recipients |

> **Note**: Columns 6, 7 are *absolute timestamps* which may leak temporal patterns to the model (phishing/normal classes have different time distributions). We recommend using only columns `[0,1,2,3,4,5,8,9,10]` as in `NODE_COLS = [0,1,2,3,4,5,8,9,10]`.
#### Edge features (4 columns)
| Col | Name | Description |
|---|---|---|
| 0 | `value` | Transaction value (wei) |
| 1 | `timestamp` | Transaction timestamp (⚠️ absolute — recommend to drop) |
| 2 | `gas_price` | Gas price (wei) |
| 3 | `gas_used` | Gas used (units) |

> **Note**: We recommend using only `EDGE_COLS = [0, 2, 3]` to avoid temporal leakage.
## 📝 Usage Example
```python
import pickle
import numpy as np
import torch
from torch_geometric.data import Data

# Load
with open("ego_graph_k2_dataset.pkl", "rb") as f:
    data = pickle.load(f)
print(f"Train: {len(data['train'])}, Val: {len(data['val'])}, Test: {len(data['test'])}")
# Convert one graph to PyG format
g = data['train'][0]
NODE_COLS = [0, 1, 2, 3, 4, 5, 8, 9, 10]  # drop absolute timestamps
EDGE_COLS = [0, 2, 3]
d = Data(
    x=torch.from_numpy(g['node_features'][:, NODE_COLS]).float(),
    edge_index=torch.from_numpy(g['edge_index']).long(),
    edge_attr=torch.from_numpy(g['edge_attr'][:, EDGE_COLS]).float(),
    y=torch.tensor([g['label']], dtype=torch.long),
)
print(f"Target: {g['target']}  label={g['label']}  nodes={g['n_nodes']}  edges={g['n_edges']}")
```
## 🛠️ Pipeline Stages (how the dataset was built)

1. **Stage 1 — Collect**: Crawl phishing addresses from Etherscan + known phishing lists
2. **Stage 2 — Normal crawl**: Collect normal (non-phishing) address candidates
3. **Stage 3 — Temporal split**: Split addresses into train/val/test by timestamp (T1, T2)
4. **Stage 9 — K=2 ego-graph construction**: For each target, extract hop-1 neighbors and hop-2 neighbors with full history up to the temporal cutoff. Features aggregated per node.
5. **Stage 5 — GNN baselines**: Train GraphSAGE / GATv2 / PEAE-style on this dataset
6. **Stage 6 — Adversarial attacks**: Evaluate robustness against 7+ transaction-level attacks
7. **Stage 7 — Defenses**: Adversarial training + Reliability-aware GNN

## 📏 Models benchmarked (reference performance)

Macro-F1 on test set (3 seeds mean ± std):

| Model | Macro-F1 | Phishing Recall | Phishing Precision |
|---|---|---|---|
| GraphSAGE | 0.8663 ± 0.0032 | 0.7772 ± 0.0110 | 0.9077 ± 0.0054 |
| GATv2 | 0.8151 ± 0.0108 | 0.6616 ± 0.0159 | 0.9068 ± 0.0106 |
| PEAE-style | **0.8813 ± 0.0028** | **0.8283 ± 0.0060** | 0.8925 ± 0.0072 |
| peaegnnv2 | 0.8564 ± 0.0040 | 0.7581 ± 0.0140 | 0.9035 ± 0.0142 |

## ⚔️ Adversarial Robustness Reference

Attack Success Rate (% target flipped) at max 1,200 injected transactions, 3 seeds mean ± std:

| Attack | peaegnnv2 | GraphSAGE | GATv2 |
|---|---|---|---|
| TOH (target → fake wallet) | 42.4 ± 3.7 | 7.1 ± 3.0 | 18.4 ± 2.5 |
| TIH (fake wallet → target) | 16.7 ± 10.3 | 5.9 ± 1.9 | **33.2 ± 7.5** |
| BPH-T2P | 41.9 ± 5.9 | 6.2 ± 2.2 | 14.7 ± 1.9 |
| STC (split + camouflage) | 23.1 ± 5.3 | 8.2 ± 3.0 | 5.0 ± 1.9 |
| **ADAPTIVE-STC-LV** | **66.6 ± 17.2** | **15.0 ± 2.0** | 6.8 ± 1.7 |

→ Best attack depends on architecture; adaptive-balance mechanism (ADAPTIVE-STC-LV) is strongest on PEAE but weakest on GATv2.

## ⚠️ Known Limitations

1. **Temporal split confound**: Phishing/normal class distribution differs across partitions (57% / 37% / 42%) due to their natural temporal distributions. We recommend dropping absolute `first_ts`, `last_ts`, `timestamp` features.
2. **Gas price is a time proxy**: Gas prices on Ethereum changed over years → models may indirectly use time info even without absolute timestamps.
3. **Max 500 txs per neighbor** was enforced during Stage 9 to limit RAM.
4. **Hop-2 is huge**: ~99% of graph nodes are hop-2 → very memory-intensive. Consider sub-sampling for low-RAM systems.
5. **Phishing labels** derived from publicly reported scam addresses — may contain some noise.

## 📖 Related Scripts

The code accompanying this dataset is in a separate repository (see `src/` folder or thesis repo). Key files:

- `stage9_k2_upgrade.py` — K=2 ego-graph builder
- `stage5_train_baselines.py` — GNN training (GraphSAGE, GATv2, PEAE-style)
- `stage5_train_baselines_paper.py` — Reproduction of Huang et al. 2024 PEAE-GNN
- `stage6_adversarial_attacks.py` — 7+ transaction-level attacks
- `stage7_defense.py` — Adversarial training + Reliability-aware GNN

## 📚 Citation

If you use this dataset, please cite:

```bibtex
@mastersthesis{ethphishgraph2026,
  title = {Evaluating and Enhancing the Robustness of Ethereum Phishing Detection Models
           Against Evasion Attacks on Transaction Graphs},
  author = {[Your Name]},
  school = {[Your University]},
  year = {2026},
  note = {EthPhishGraph-2026 dataset available at https://huggingface.co/datasets/[your-username]/EthPhishGraph-2026}
}
```

## 🔗 Related Papers

- Wen et al. 2022, "Hide and Seek: An Adversarial Hiding Approach Against Phishing Detection on Ethereum", IEEE TCSS — source of TOH, TIH, BPH attacks
- Huang et al. 2024, "PEAE-GNN: Phishing Detection on Ethereum via Augmentation Ego-Graph Based on Graph Neural Network", IEEE TCSS — PEAE architecture
- Frankart et al. 2025, "The Feature-Space Illusion", arXiv — related adversarial robustness work on blockchain GNNs

## 📜 License

**CC-BY-4.0** (Creative Commons Attribution 4.0 International).

Ethereum on-chain data is public and permissionless. Phishing/scam labels aggregated from public threat intelligence sources (Etherscan phishing tags, PhishTank, community-reported lists).

## 🙏 Acknowledgments

Data collected via Etherscan API. Compare dataset D2 from XBlock project ([link](https://xblock.pro)).

	---
	license: cc-by-4.0
	task_categories:
	- graph-ml
	- node-classification
	tags:
	- ethereum
	- blockchain
	- phishing-detection
	- gnn
	- graph-neural-network
	- adversarial-robustness
	- fraud-detection
	- cryptocurrency
	size_categories:
	- 1K<n<10K
	language:
	- en
	pretty_name: EthPhishGraph-2026
	---

	# EthPhishGraph-2026

	Ego-graph K=2 dataset of Ethereum phishing accounts for Graph Neural Network based phishing detection and adversarial robustness research.

	## 📊 Dataset Statistics

	\| \| Count \| % Phishing \|
	\|---\|---\|---\|
	\| Total targets \| 7,036 \| 50.0% \|
	\| Phishing \| 3,518 \| - \|
	\| Normal \| 3,518 \| - \|

	### Temporal split (60/20/20)

	\| Partition \| Count \| Phishing ratio \| Period \|
	\|---\|---\|---\|---\|
	\| Train \| 4,221 \| 56.8% \| ≤ T1 = 2018-04-23 \|
	\| Val \| 1,407 \| 37.3% \| T1 → T2 = 2018-07-12 \|
	\| Test \| 1,408 \| 42.2% \| > T2 \|

	### Graph size

	\| Partition \| Avg nodes \| Avg edges \|
	\|---\|---\|---\|
	\| Train \| 2,318 \| 5,425 \|
	\| Val \| 2,482 \| 5,376 \|
	\| Test \| 3,677 \| 7,297 \|

	> hop-2 chiếm ~99% số node mỗi graph. Số cạnh (edges) tính cả 2 chiều trong hop-1/hop-2.

	## 📁 Dataset Structure

	```
	EthPhishGraph-2026/
	├── README.md # This file
	├── LICENSE # CC-BY-4.0
	├── ego_graph_k2_dataset.pkl # 4.5 GB — Main pickle (train/val/test splits)
	├── temporal_split.csv # 449 KB — Address → (label, partition, anchor_ts)
	├── temporal_split_meta.json # — Split metadata (T1, T2, counts)
	├── per_target_neighbors.pkl # 11 MB — Precomputed hop-1 neighbor lists
	├── unique_hop1_neighbors.csv # — Deduplicated neighbor addresses
	├── feat_stats.pt # — Normalization stats (mean, std) from TRAIN only
	├── bad_files.txt # — Known bad hop-2 files to skip
	├── raw_txs/ # Raw transaction JSONs (for Stage 6 attacks)
	│ ├── phishing_hop1_txs.tar.gz # ~50 MB — Phishing accounts' hop-1 txs
	│ ├── normal_hop1_txs.tar.gz # ~100 MB — Normal accounts' hop-1 txs
	│ └── hop2_neighbor_txs.tar.gz # ~5 GB — Hop-2 neighbor txs (compressed from 20 GB)
	└── misc/ # Optional artifacts (not needed for basic use)
	├── stage1_outputs.tar.gz
	├── stage4_imbalanced.tar.gz
	└── stage8_crosscheck_D2.tar.gz
	```

	## 🔬 Data Schema

	### `ego_graph_k2_dataset.pkl` (main file)

	```python
	import pickle
	with open("ego_graph_k2_dataset.pkl", "rb") as f:
	data = pickle.load(f)
	# data = {"train": [...], "val": [...], "test": [...]}
	```

	Each entry is a `dict`:

	\| Key \| Type \| Shape \| Description \|
	\|---\|---\|---\|---\|
	\| `target` \| `str` \| — \| Ethereum address `0x...` \|
	\| `label` \| `int` \| — \| 0 = normal, 1 = phishing \|
	\| `target_idx` \| `int` \| — \| Position of target in node list \|
	\| `n_nodes` \| `int` \| — \| Total nodes in the ego-graph \|
	\| `n_edges` \| `int` \| — \| Total directed edges \|
	\| `n_hop1` \| `int` \| — \| Number of hop-1 neighbors \|
	\| `node_features` \| `np.ndarray` \| `[N, 11]` \| See Node features below \|
	\| `edge_index` \| `np.ndarray` \| `[2, E]` \| COO format (source, destination) \|
	\| `edge_attr` \| `np.ndarray` \| `[E, 4]` \| See Edge features below \|
	\| `cutoff` \| `int` \| — \| Unix timestamp used as temporal cutoff \|

	#### Node features (11 columns)

	\| Col \| Name \| Description \|
	\|---\|---\|---\|
	\| 0 \| `in_count` \| Number of incoming transactions \|
	\| 1 \| `out_count` \| Number of outgoing transactions \|
	\| 2 \| `in_value_sum` \| Total incoming value (wei) \|
	\| 3 \| `out_value_sum` \| Total outgoing value (wei) \|
	\| 4 \| `in_value_mean` \| Mean incoming value (wei) \|
	\| 5 \| `out_value_mean` \| Mean outgoing value (wei) \|
	\| 6 \| `first_ts` \| First tx timestamp (⚠️ absolute — recommend to drop) \|
	\| 7 \| `last_ts` \| Last tx timestamp (⚠️ absolute — recommend to drop) \|
	\| 8 \| `active_span` \| `last_ts - first_ts` (relative, safe) \|
	\| 9 \| `unique_in_peers` \| Number of distinct senders \|
	\| 10 \| `unique_out_peers` \| Number of distinct recipients \|

	> Note: Columns 6, 7 are absolute timestamps which may leak temporal patterns to the model (phishing/normal classes have different time distributions). We recommend using only columns `[0,1,2,3,4,5,8,9,10]` as in `NODE_COLS = [0,1,2,3,4,5,8,9,10]`.

	#### Edge features (4 columns)

	\| Col \| Name \| Description \|
	\|---\|---\|---\|
	\| 0 \| `value` \| Transaction value (wei) \|
	\| 1 \| `timestamp` \| Transaction timestamp (⚠️ absolute — recommend to drop) \|
	\| 2 \| `gas_price` \| Gas price (wei) \|
	\| 3 \| `gas_used` \| Gas used (units) \|

	> Note: We recommend using only `EDGE_COLS = [0, 2, 3]` to avoid temporal leakage.

	## 📝 Usage Example

	```python
	import pickle
	import numpy as np
	import torch
	from torch_geometric.data import Data

	# Load
	with open("ego_graph_k2_dataset.pkl", "rb") as f:
	data = pickle.load(f)
	print(f"Train: {len(data['train'])}, Val: {len(data['val'])}, Test: {len(data['test'])}")

	# Convert one graph to PyG format
	g = data['train'][0]
	NODE_COLS = [0, 1, 2, 3, 4, 5, 8, 9, 10] # drop absolute timestamps
	EDGE_COLS = [0, 2, 3]

	d = Data(
	x=torch.from_numpy(g['node_features'][:, NODE_COLS]).float(),
	edge_index=torch.from_numpy(g['edge_index']).long(),
	edge_attr=torch.from_numpy(g['edge_attr'][:, EDGE_COLS]).float(),
	y=torch.tensor([g['label']], dtype=torch.long),
	)
	print(f"Target: {g['target']} label={g['label']} nodes={g['n_nodes']} edges={g['n_edges']}")
	```

	## 🛠️ Pipeline Stages (how the dataset was built)

	1. Stage 1 — Collect: Crawl phishing addresses from Etherscan + known phishing lists
	2. Stage 2 — Normal crawl: Collect normal (non-phishing) address candidates
	3. Stage 3 — Temporal split: Split addresses into train/val/test by timestamp (T1, T2)
	4. Stage 9 — K=2 ego-graph construction: For each target, extract hop-1 neighbors and hop-2 neighbors with full history up to the temporal cutoff. Features aggregated per node.
	5. Stage 5 — GNN baselines: Train GraphSAGE / GATv2 / PEAE-style on this dataset
	6. Stage 6 — Adversarial attacks: Evaluate robustness against 7+ transaction-level attacks
	7. Stage 7 — Defenses: Adversarial training + Reliability-aware GNN

	## 📏 Models benchmarked (reference performance)

	Macro-F1 on test set (3 seeds mean ± std):

	\| Model \| Macro-F1 \| Phishing Recall \| Phishing Precision \|
	\|---\|---\|---\|---\|
	\| GraphSAGE \| 0.8663 ± 0.0032 \| 0.7772 ± 0.0110 \| 0.9077 ± 0.0054 \|
	\| GATv2 \| 0.8151 ± 0.0108 \| 0.6616 ± 0.0159 \| 0.9068 ± 0.0106 \|
	\| PEAE-style \| 0.8813 ± 0.0028 \| 0.8283 ± 0.0060 \| 0.8925 ± 0.0072 \|
	\| peaegnnv2 \| 0.8564 ± 0.0040 \| 0.7581 ± 0.0140 \| 0.9035 ± 0.0142 \|

	## ⚔️ Adversarial Robustness Reference

	Attack Success Rate (% target flipped) at max 1,200 injected transactions, 3 seeds mean ± std:

	\| Attack \| peaegnnv2 \| GraphSAGE \| GATv2 \|
	\|---\|---\|---\|---\|
	\| TOH (target → fake wallet) \| 42.4 ± 3.7 \| 7.1 ± 3.0 \| 18.4 ± 2.5 \|
	\| TIH (fake wallet → target) \| 16.7 ± 10.3 \| 5.9 ± 1.9 \| 33.2 ± 7.5 \|
	\| BPH-T2P \| 41.9 ± 5.9 \| 6.2 ± 2.2 \| 14.7 ± 1.9 \|
	\| STC (split + camouflage) \| 23.1 ± 5.3 \| 8.2 ± 3.0 \| 5.0 ± 1.9 \|
	\| ADAPTIVE-STC-LV \| 66.6 ± 17.2 \| 15.0 ± 2.0 \| 6.8 ± 1.7 \|

	→ Best attack depends on architecture; adaptive-balance mechanism (ADAPTIVE-STC-LV) is strongest on PEAE but weakest on GATv2.

	## ⚠️ Known Limitations

	1. Temporal split confound: Phishing/normal class distribution differs across partitions (57% / 37% / 42%) due to their natural temporal distributions. We recommend dropping absolute `first_ts`, `last_ts`, `timestamp` features.
	2. Gas price is a time proxy: Gas prices on Ethereum changed over years → models may indirectly use time info even without absolute timestamps.
	3. Max 500 txs per neighbor was enforced during Stage 9 to limit RAM.
	4. Hop-2 is huge: ~99% of graph nodes are hop-2 → very memory-intensive. Consider sub-sampling for low-RAM systems.
	5. Phishing labels derived from publicly reported scam addresses — may contain some noise.

	## 📖 Related Scripts

	The code accompanying this dataset is in a separate repository (see `src/` folder or thesis repo). Key files:

	- `stage9_k2_upgrade.py` — K=2 ego-graph builder
	- `stage5_train_baselines.py` — GNN training (GraphSAGE, GATv2, PEAE-style)
	- `stage5_train_baselines_paper.py` — Reproduction of Huang et al. 2024 PEAE-GNN
	- `stage6_adversarial_attacks.py` — 7+ transaction-level attacks
	- `stage7_defense.py` — Adversarial training + Reliability-aware GNN

	## 📚 Citation

	If you use this dataset, please cite:

	```bibtex
	@mastersthesis{ethphishgraph2026,
	title = {Evaluating and Enhancing the Robustness of Ethereum Phishing Detection Models
	Against Evasion Attacks on Transaction Graphs},
	author = {[Your Name]},
	school = {[Your University]},
	year = {2026},
	note = {EthPhishGraph-2026 dataset available at https://huggingface.co/datasets/[your-username]/EthPhishGraph-2026}
	}
	```

	## 🔗 Related Papers

	- Wen et al. 2022, "Hide and Seek: An Adversarial Hiding Approach Against Phishing Detection on Ethereum", IEEE TCSS — source of TOH, TIH, BPH attacks
	- Huang et al. 2024, "PEAE-GNN: Phishing Detection on Ethereum via Augmentation Ego-Graph Based on Graph Neural Network", IEEE TCSS — PEAE architecture
	- Frankart et al. 2025, "The Feature-Space Illusion", arXiv — related adversarial robustness work on blockchain GNNs

	## 📜 License

	CC-BY-4.0 (Creative Commons Attribution 4.0 International).

	Ethereum on-chain data is public and permissionless. Phishing/scam labels aggregated from public threat intelligence sources (Etherscan phishing tags, PhishTank, community-reported lists).

	## 🙏 Acknowledgments

	Data collected via Etherscan API. Compare dataset D2 from XBlock project ([link](https://xblock.pro)).