Partitioning directed graphs based on modularity and information flow

Although models of the behavior of individual neurons and synapses are now well established, understanding the way in which they cooperate in large ensembles remains a major scientific challenge. We present two novel graph theory methods to study cortical interactions and image the highly organized structure of large scale networks. First, we present a new method to partition directed graphs into modules, based on modularity and an expected network conditioned on the in- and out-degrees of all nodes. We also propose a method to segment graphs based on information flow. These methods are combined to study the community structure of brain networks and information flow within the modules.