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Graph Evolution: Densification and Shrinking Diameters.
Leskovec, J., Kleinberg, J. & Faloutsos, C. Graph evolution: Densification and shrinking diameters.
Fig. 16 Redundant rebroadcasting in flooding modeled as graph evolution with time.
As future work, we wish to improve our algorithm by means of further optimization and to enhance the tools by providing a standalone module for live visualization of graph evolution.
Yet, for large networks in which the rate of structural changes in time could be very high, the task of determining the nodes and edges that can represent and transmit the salient structural properties of the network at a certain time is crucial to produce meaningful visualizations of the graph evolution.
Friedrich and Eades [34] defined high-level guidelines for a good visualization of graph evolution with animations, including uniform, smooth and symmetrical movement of graph elements, with minimization of edge crossings and overviewing some techniques that make the visualization more enjoyable, such as fadeout deletion of nodes.
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The eigenvalues of the normalized graph Laplacian not only capture the global properties of a network, but also local structures that are produced by graph evolutions (like motif duplication or joining).
Despite static visualizations based on time-windows [23], alluvial diagrams [27], or matrices [28] [30] have been explored as solutions to capture the graph evolution, dynamic graph drawing remains the technique that has attracted more interest in the research community so far.
The results presented in a set of tables and graphs illustrate the evolution of micromorphological research during the 20th century.
Figure 9 This graph shows evolution of exposed (left) and removed (right) population.
We first graph the evolution of the average hourly wages that agents in the fifth quintile earn relative to those earned by agents in the first quintile.
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