@article {48495,
	title = {Semiparametric spectral modeling of the Drosophila connectome.},
	journal = {arXiv},
	year = {2017},
	month = {2017 May 9},
	pages = {1705.03297},
	abstract = {<p>We present semiparametric spectral modeling of the complete larval Drosophila mushroom body connectome. Motivated by a thorough exploratory data analysis of the network via Gaussian mixture modeling (GMM) in the adjacency spectral embedding (ASE) representation space, we introduce the latent structure model (LSM) for network modeling and inference. LSM is a generalization of the stochastic block model (SBM) and a special case of the random dot product graph (RDPG) latent position model, and is amenable to semiparametric GMM in the ASE representation space. The resulting connectome code derived via semiparametric GMM composed with ASE captures latent connectome structure and elucidates biologically relevant neuronal properties.</p>
},
	url = {https://arxiv.org/abs/1705.03297},
	author = {Priebe, Carey E. and Park, Youngser and Tang, Minh and Athreya, Avanti and Lyzinski, Vince and Vogelstein, Joshua T and Qin, Yichen and Cocanougher, Benjamin and Eichler, Katharina and Zlatic, Marta and Cardona, Albert}
}
