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

Figure 3

Overlapping modules of a word-association network.

Modules of the University of South Florida word association network [20] were determined using the LinkLand influence function calculation method and the TotalHill module membership assignment method. 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). The network was laid out using the Kamada-Kawai algorithm of Graphviz [48] and visualized using a custom program written in Python language using OpenGL graphics. Links were colored in proportion to the colors of the modules they belong. Panel A: modules around the antagonym word, “terrific”. Panel B: modules around the heteronym word, “content”. In addition to the selected words “terrific” and “content” similar words above a similarity threshold of 10% are also shown with a contrast corresponding to their degree of similarity. The extent of similarity between two words was calculated as the sum of the two pair-wise minima of their unity-normalized module membership vector giving the membership assignment strength of the given word to all modules of the network (for more details see Section V.6.e. in the Electronic Supplementary Material S1).

Figure 3

doi: https://doi.org/10.1371/journal.pone.0012528.g003