Figure 5. .

Edge-centric approaches allow for edges to be clustered directly, which can reveal mesoscale organization at the edge level. (A) Node-based clustering results in groupings of nodes that are commonly modular, and can be visualized as dense squares on the diagonal of an adjacency matrix; edge-based clustering results in groupings of edges with a common property, and can be visualized by coloring the adjacency matric with community affiliation. (B) Edge community overlap can be indexed by a node-level measurement of entropy, which characterizes the distribution of discrete communities connecting to each node. (C) A 10-community clustering of edge functional connectivity visualized as an adjacency matrix (left), and sorted by community entropy (right); the sorted matrix displays a “banding” pattern, which demonstrates a difference between high- and low-entropy nodes (figure adapted from Faskowitz et al., 2020). (D) The edge cluster similarity matrix indicates the similarity of edge community profiles, which are rows (or columns) of the edges community matrix (as in C); this matrix indicates the varying levels of edge community diversity contained within canonical functional systems (figure adapted from Faskowitz et al., 2020).