Central limit theorems for local network statistics

P-A. Maugis

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arXiv · 2006.15738

Central limit theorems for local network statistics

P-A. Maugis

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Abstract

Subgraph counts - in particular the number of occurrences of small shapes such as triangles - characterize properties of random networks, and as a result have seen wide use as network summary statistics. However, subgraphs are typically counted globally, and existing approaches fail to describe vertex-specific characteristics. On the other hand, rooted subgraph counts - counts focusing on any given vertex's neighborhood - are fundamental descriptors of local network properties. We derive the asymptotic joint distribution of rooted subgraph counts in inhomogeneous random graphs, a model which generalizes many popular statistical network models. This result enables a shift in the statistical analysis of large graphs, from estimating network summaries, to estimating models linking local network structure and vertex-specific covariates. As an example, we consider a school friendship network and show that local friendship patterns are significant predictors of gender and race.

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BibTeX RIS

P-A. Maugis. 2020-06-28. Central limit theorems for local network statistics. https://arxiv.org/abs/2006.15738

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