Latent space approaches to aggregate network data

Large-scale network data can pose computational challenges, be expensive to acquire, and compromise the privacy of individuals in social networks. We show that the locations and scales of latent space cluster models can be inferred from the number of connections between groups alone. We demonstrate this modelling approach using synthetic data and apply it to friendships between students collected as part of the Add Health study, eliminating the need for node-level connection data. The method thus protects the privacy of individuals and simplifies data sharing. It also offers performance advantages over node-level latent space models because the computational cost scales with the number of clusters rather than the number of nodes.

• Publication year

  2023

• Venue

  arXiv.org

• Publication date

  2023-03-15

• Fields of study

  Sociology, Physics, Computer Science, Mathematics

• Identifiers
  DOI 10.48550/arXiv.2303.08338arXiv 2303.08338
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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