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Tunable network properties with Hamill and Gilbert's Social Circles generator
Cristina Chueca Del Cerro1, Jennifer Badham1
1Department of Sociology, Durham University, Mill Hill Lane, Durham, DH1 3LB UK.
Applied Network Science
|December 22, 2025
Summary
The Social Circles algorithm generates synthetic networks but cannot independently control key properties like clustering coefficient. This limits its use as a general network generator without further refinement.
Area of Science:
- Computational Social Science
- Network Science
- Agent-Based Modeling
Background:
- The Social Circles algorithm was developed to create synthetic social networks.
- It aims to replicate properties of real-world networks like skewed degree distribution and clustering.
Purpose of the Study:
- To evaluate the Social Circles algorithm's effectiveness as a general network generator.
- To systematically analyze the impact of algorithm parameters on network structural properties.
Main Methods:
- Varied agent social reaches, distribution of social reaches, and node density.
- Examined relationships between algorithm parameters and network properties such as edge density, path lengths, assortativity, and centrality measures.
Main Results:
- Algorithm parameters predictably influenced edge density and centrality measures.
- Longer reaches correlated with denser networks, shorter paths, and less variation in centrality.
- Limited ability to control network properties independently; clustering coefficient was insensitive to inputs.
Conclusions:
- Social Circles cannot be used as a general network generator in its current form.
- It may serve as a starting point for generating networks with basic social structure, but requires further refinement for specific properties.
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