title: A joint graph inference case study: the C. elegans chemical and electrical connectomes.
abstract: We investigate joint graph inference for the chemical and electrical connectomes of the Caenorhabditis elegans roundworm. The C. elegans connectomes consist of [Formula: see text] non-isolated neurons with known functional attributes, and there are two types of synaptic connectomes, resulting in a pair of graphs. We formulate our joint graph inference from the perspectives of seeded graph matching and joint vertex classification. Our results suggest that connectomic inference should proceed in the joint space of the two connectomes, which has significant neuroscientific implications.
authors.affiliation: Software and Service Group, Intel Corporation , Hillsboro, OR, USA.
authors.forename: Li
authors.initials: L
authors.lastname: Chen
authors.affiliation: Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University , Baltimore, MD, USA.
authors.forename: Joshua T
authors.initials: JT
authors.lastname: Vogelstein
authors.affiliation: Johns Hopkins University Human Language Technology Center of Excellence , Baltimore, MD, USA.
authors.forename: Vince
authors.initials: V
authors.lastname: Lyzinski
authors.affiliation: Department of Applied Mathematics and Statistics, Johns Hopkins University , Baltimore, MD, USA.
authors.forename: Carey E
authors.initials: CE
authors.lastname: Priebe
keywords: fusion inference; joint graph inference; joint vertex classification; neural connectome; omnibus embedding; seeded graph matching