• Corpus ID: 88516110

A Class of Temporal Hierarchical Exponential Random Graph Models for Longitudinal Network Data

@article{Cao2017ACO,
  title={A Class of Temporal Hierarchical Exponential Random Graph Models for Longitudinal Network Data},
  author={Ming Cao},
  journal={arXiv: Methodology},
  year={2017}
}
  • Ming Cao
  • Published 3 April 2017
  • Computer Science
  • arXiv: Methodology
As a representation of relational data over time series, longitudinal networks provide opportunities to study link formation processes. However, networks at scale often exhibits community structure (i.e. clustering), which may confound local structural effects if it is not considered appropriately in statistical analysis. To infer the (possibly) evolving clusters and other network structures (e.g. degree distribution and/or transitivity) within each community, simultaneously, we propose a class… 

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