Synthetic Social Graph of Kaggle Users and Competitions

Kaggle Competition Graph Dataset is a synthetic social graph connecting Kaggle users, teams, and AI competitions. It is designed for graph-based analysis and machine learning tasks.

Use Cases

• Apply Graph Neural Networks to analyze relationships between users, teams, and competitions.
• Perform community detection to identify clusters of collaborating users or teams within the Kaggle ecosystem.
• Conduct link prediction to forecast potential team formations or user participation in future competitions.
• Visualize the network structure for exploratory analysis of the synthetic Kaggle community.

Strengths

• Dataset is explicitly designed for graph-based tasks like Graph Neural Networks and social network analysis.
• Synthetic nature allows for controlled experimentation without privacy concerns from real user data.
• Covers multiple entity types relevant to a data science platform: users, teams, and competitions.

Limitations

• Data is synthetic, so patterns and insights may not reflect real-world Kaggle community dynamics.
• Key quantitative details like node count, edge count, and graph density are unknown.
• The absence of column definitions or sample data makes initial data exploration and validation necessary.

Provenance

Source

Kaggle

Collection Method

Synthetically generated.

Time Range

2026