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Community Landscapes: An Integrative Approach to Determine Overlapping Network Module Hierarchy, Identify Key Nodes and Predict Network Dynamics

Figure 6

Prediction of the dynamical behavior of network nodes: segregation of date- and party-hubs based on their modular overlaps.

Overlapping modules of the yeast protein-protein interaction network of Ekman et al. [25] were identified using the LinkLand influence function calculation method with the TotalHill module membership assignment method using the modular structure of the lowest level of hierarchy. During the post-processing of the module assignment, we merged the modules with ProportionalHill module membership assignment-based correlation higher than 0.9 (see Section VI. in the Electronic Supplementary Material S1, we received similar results without this merging process; data not shown). Panel A: 3D view of the yeast protein-protein interaction network. The underlying 2D network layout was set by the Kamada-Kawai algorithm. The vertical positions reflect the community landscape values of the nodes on a linear scale. Nodes were colored as the module of their maximum membership. Panel B: centrality and bridgeness of yeast date- and party-hubs. Hubs having more than 8 neighbors and non-hubs with less neighbors were positioned on the scattergram according to their ModuLand centrality (x-axis, the height of the community landscape) and ModuLand bridgeness (y-axis) as defined in Section V.6.d. in the Electronic Supplementary Material S1. Date- and party-hubs are marked with red circles and blue triangles, respectively, while non-hub proteins are represented by gray crosses. The inset shows a double logarithmic plot of hubs with large centrality.

Figure 6