#Network analytics in the age of #BigData
http://science.ScienceMag.org/content/353/6295/123.full Emphasizes analyzing connectivity of graph structures (eg motifs) v nodes
QT:{{”
To mine the wiring patterns of networked data and uncover the functional organization, it is not enough to consider only simple descriptors, such as the number of interactions that each entity (node) has with other entities (called node degree), because two networks can be identical in such simple descriptors, but have a very different connectivity structure (see the figure). Instead, Benson et al. use higher-order descriptors called graphlets (e.g., a triangle) that are based on small subnetworks obtained on a subset of nodes in the data that contain all interactions that appear in the data (3). They identify network regions rich in instances of a particular graphlet type, with few of the instances of the particular graphlet crossing the boundaries of the regions. If the graphlet type is specified in advance, the method can uncover the nodes interconnected by it, which enabled Benson et al. to group together 20 neurons in the nematode worm neuronal network that are known to control a particular type of movement. In this way, the method unifies the local wiring patterning with higher-order structural modularity imposed by it, uncovering higher-order functional regions in networked data. “}}
