README.md

---
license: cc-by-nc-4.0
pretty_name: Free Synthetic Social Network (100M Edges)
size_categories:
  - 10M<n<100M
tags:
  - social-network
  - graph
  - graph-ml
  - synthetic-data
  - recommendation-systems
  - network-analysis
---

# Free Synthetic Social Network (100M Edges)

A synthetic social-network graph — 3,000,000 users and 100,000,000 directed connections between them (follow, friend, block, mute). Built for graph ML, recommendation-system prototyping, community-detection, and social-network-analysis workflows.

No real users, accounts, or platform data were used — every record is generated from scratch.

## Schema

Two related tables: `social_network_nodes_3M.parquet` (users) and `social_network_edges_100M.parquet` (connections between them).

**Nodes** (`social_network_nodes_3M.parquet`)

| Column | Type | Description |
|---|---|---|
| user_id | string | Unique identifier for the user |
| region_id | int | Synthetic region cluster (0-19) the user belongs to |
| account_type | string | personal, business, or creator |
| join_date | date | Date the account was created |
| follower_count | int | Number of incoming edges (matches the edges table exactly) |
| following_count | int | Number of outgoing edges (matches the edges table exactly) |
| post_count | int | Total posts, correlated with account age and account type |

**Edges** (`social_network_edges_100M.parquet`)

| Column | Type | Description |
|---|---|---|
| edge_id | string | Unique identifier for the connection |
| source_user_id | string | User who initiated the connection |
| target_user_id | string | User on the receiving end |
| edge_type | string | follow, friend, block, or mute |
| created_date | date | Date the connection was created (always on/after both users' join dates) |
| interaction_weight | float | Relative interaction intensity between the pair (likes/comments/DMs proxy) |

## Format

Two Parquet files, Snappy-compressed.

## Quick start

```python
import pandas as pd
nodes = pd.read_parquet("social_network_nodes_3M.parquet")
edges = pd.read_parquet("social_network_edges_100M.parquet")
print(nodes.head())
print(edges.head())

# Or with duckdb for larger-than-memory queries
import duckdb
duckdb.sql("SELECT edge_type, COUNT(*) FROM 'social_network_edges_100M.parquet' GROUP BY edge_type")

# Or with the datasets library
from datasets import load_dataset
ds = load_dataset("ziadatalabs/FreeSyntheticSocialNetwork100M")
```

## Notes

- Degree distribution follows a realistic power-law shape rather than uniform-random: most users have a small number of connections, while a small number of highly-influential users accumulate a disproportionate share of followers — the same long-tail pattern seen in real social graphs.
- Connections cluster regionally: 80% of edges form between users in the same `region_id`, 20% cross regions, giving the graph genuine community structure instead of a flat random mesh.
- `follower_count` and `following_count` in the nodes table are computed directly from the edges table, so they match exactly — useful for validating graph-processing pipelines against ground truth.
- `created_date` is always on or after both the source and target users' `join_date`, and `interaction_weight` grows with edge age, so activity patterns stay internally consistent.
- This dataset is part of a growing collection of free synthetic datasets across security, finance, healthcare operations, retail, geospatial, gaming, and other domains.

## License & Usage

Licensed under CC BY-NC 4.0 (Creative Commons Attribution-NonCommercial 4.0). Free to use for personal, research, and educational purposes with attribution. Not licensed for commercial use.

Published by Zia Data Labs. More free synthetic datasets at huggingface.co/ziadatalabs.

Want more free datasets? Hit the ❤️ and follow. And we take requests — tell us what synthetic data you need, and we'll build it.

Contact: zia.data.team@protonmail.com