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A Blue Start: A large-scale pairwise and higher-order social network dataset
Alyssa Hasegawa Smith1, Ilya Amburg2, Sagar Kumar3,4
1Network Science Institute, Northeastern University, Boston, Massachusetts, USA. smith.alyss@northeastern.edu.
Scientific Data
|March 3, 2026
Summary
Researchers introduce "A Blue Start," a novel dataset of large-scale social networks from the Bluesky platform. This resource captures both pairwise and higher-order interactions, crucial for understanding complex social dynamics and disease spread.
Area of Science:
- Social Network Analysis
- Computational Social Science
- Mathematical Epidemiology
Background:
- Large-scale networks are foundational in social systems, epidemiology, and biology.
- Real-world interactions often involve higher-order (group) dynamics, not just pairwise ties.
- A gap exists between higher-order models and available data, hindering validation.
Purpose of the Study:
- To introduce a novel, large-scale dataset bridging pairwise and higher-order network data.
- To provide a resource for studying group formation and spreading processes in social systems.
- To leverage the Bluesky social media platform's unique features for network research.
Main Methods:
- Utilized the Bluesky social media platform's open API.
- Collected data on user accounts, pairwise following relationships, and user-curated groups ('starter packs').
- Compiled a dataset named 'A Blue Start' comprising 39.7M users, 2.4B relationships, and 365.8K groups.
Main Results:
- The 'A Blue Start' dataset contains 39.7 million user accounts.
- The dataset includes 2.4 billion pairwise following relationships.
- The dataset comprises 365.8 thousand groups representing higher-order social structures.
Conclusions:
- The 'A Blue Start' dataset is a significant resource for higher-order network analysis.
- This dataset facilitates research into social dynamics, disease spread, and information dissemination.
- It enables the study of mechanisms bridging pairwise and group-level interactions.
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