Best Representative Area.

<p>Best Representative Area.</p

Topological properties of the networks.

<p>Results for random graph models are averaged over 20 realizations. For the directed (undirected) network the largest strong (weak) connected component is used.</p

Vertices are laid out using the hierarchical map and radial layout of Fig 1.

<p>The pie-chart for each vertex is based on unnormalized counts of its brokerage type. In the pie-chart red color is for coordinator, blue for gatekeeper, green for representative, yellow for consultant and cyan for liaison. The black splines are the intra-lobe connectivity. The blackened vertices are those that have either no connectivity or have been assigned no roles by UCINET.</p

Best Consultant Area.

<p>Best Consultant Area.</p

Disparity plots for undirected Macaque network and its null models.

<p>(a) Top plot: Black empty squares plot disparity against SV for all local subnetworks of the Macaque network. The mean SV of these subnetworks for each disparity value is plotted using the blue curve with filled circles. (b) Middle plot: the blue curve with crosses plots the mean degree of the hub of the subnetworks with the same disparity value. Mean values in the top and middle plot are plotted for different null models using the color legend shown in the bottom plot. (c) Bottom plot: Normalized histogram of local subnetworks for each disparity value, for each network. For ISO, MS, B-MS null models results are averaged over 20 realizations.</p

Analysing Local Sparseness in the Macaque Brain Network - Fig 8

<p>In (a) there are very few open triads generating a low workload among the vertices. Regardless of whether the spokes and their hubs have the same attribute or not, the workload closure (WCC) will be high because of the low workload. In (b) there are more open triads, but all are being served by hubs having the same attribute as their spokes. Hence though the workload is high, the WCC is also high. Note that in this paper attribute, is the membership of a super-area.</p

Best Liason Area.

<p>Best Liason Area.</p

Top-10 hubs with star-like motifs.

<p>Top-10 hubs with star-like motifs.</p

The local subnetwork of hub vertex <i>v</i>_<i>k</i> modeled as a tripartiate graph—<i>S</i>_<i>k</i> + <i>C</i>_<i>k</i> vertices in the first partition, <i>v</i>_<i>k</i> vertex in the second partition, and <i>T</i>_<i>k</i> + <i>C</i>_<i>k</i> vertices in the third partition.

<p>The intra-spokes edges are directed from the vertices in the first partition to the vertices in the third partition, shown by dotted arrows.</p

Workload Closure Coefficient plot.

<p>For each super-area “Density” is the number of connections within the super-area divided by the possible number of connections, “CC” is the clustering coefficient for the intra super-area subnetwork, and “WCC” is workload closure coefficient:- the number of closed pair of spokes divided by the total number of pair of spokes in a sub-area. The plot has been drawn such that WCC is sorted in ascending order, and the remaining two measures are sorted in the same order.</p