# Projective, Sparse, and Learnable Latent Position Network Models

@article{Spencer2017ProjectiveSA, title={Projective, Sparse, and Learnable Latent Position Network Models}, author={Neil A. Spencer and Cosma Rohilla Shalizi}, journal={arXiv: Statistics Theory}, year={2017} }

When modeling network data using a latent position model, it is typical to assume that the nodes' positions are independently and identically distributed. However, this assumption implies the average node degree grows linearly with the number of nodes, which is inappropriate when the graph is thought to be sparse. We propose an alternative assumption---that the latent positions are generated according to a Poisson point process---and show that it is compatible with various levels of sparsity… CONTINUE READING

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