  Graphs are quickly emerging as a leading abstraction for the representation
of data. One important application domain originates from an emerging
discipline called "connectomics". Connectomics studies the brain as a graph;
vertices correspond to neurons (or collections thereof) and edges correspond to
structural or functional connections between them. To explore the variability
of connectomes---to address both basic science questions regarding the
structure of the brain, and medical health questions about psychiatry and
neurology---one can study the topological properties of these brain-graphs. We
define multivariate glocal graph invariants: these are features of the graph
that capture various local and global topological properties of the graphs. We
show that the collection of features can collectively be computed via a
combination of daisy-chaining, sparse matrix representation and computations,
and efficient approximations. Our custom open-source Python package serves as a
back-end to a Web-service that we have created to enable researchers to upload
graphs, and download the corresponding invariants in a number of different
formats. Moreover, we built this package to support distributed processing on
multicore machines. This is therefore an enabling technology for network
science, lowering the barrier of entry by providing tools to biologists and
analysts who otherwise lack these capabilities. As a demonstration, we run our
code on 120 brain-graphs, each with approximately 16M vertices and up to 90M
edges.
