# Inference for multiple heterogeneous networks with a common invariant subspace

@article{Arroyo2019InferenceFM, title={Inference for multiple heterogeneous networks with a common invariant subspace}, author={Jes{\'u}s Arroyo and A. Athreya and Joshua Cape and Guodong Chen and C. Priebe and J. Vogelstein}, journal={ArXiv}, year={2019}, volume={abs/1906.10026} }

The development of models for multiple heterogeneous network data is of critical importance both in statistical network theory and across multiple application domains. Although single-graph inference is well-studied, multiple graph inference is largely unexplored, in part because of the challenges inherent in appropriately modeling graph differences and yet retaining sufficient model simplicity to render estimation feasible. This paper addresses exactly this gap, by introducing a new model, the… Expand

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