Figure 2. .

Network science offers a wide range of tools and methods to analyze brain networks. (A) Many common brain network analyses extract information about the nodes (i.e., node-centric), such as their centrality or modular groupings; edge-centric brain network analyses can annotate the edges with information, such as the proportion of shortest paths that pass through an edge or the effect of an edge’s removal on global network statistics. (B) Line graphs are representations of a network that capture how edges are connected to each other via nodes, as depicted in this diagram. (C) Clustering an edge-edge network representation, in which network incidence (e.g., line graph) or pairwise edge similarity is assessed, results in an edge community structure; by affiliating each edge with a cluster, each node is associated multiple (or overlapping) communities (figure reproduced from M. A. de Reus, Saenger, Kahn, & van den Heuvel, 2014, with permission from The Royal Society, UK). (D) The pairwise similarity between edges can be assessed by correlating feature sets at the edges, such as multiple tractography streamline weights or functional correlation measures taken during distinct tasks (figure adapted from Faskowitz, Tanner, Misic, & Betzel, 2021).