ziadatalabs/FreeSyntheticSocialNetwork100M

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

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