Knuth, The Stanford GraphBase: A Platform for Combinatorial Computing, Addison-Wesley, Reading, MA, 1993). In this and the next couple of posts, I will show a few ways to quickly visualize networks directly from R, using different R packages.įor illustration purposes, I used a weighted network of characters’ coappearances in Victor Hugo’s novel “Les Miserables” (from D. Clearly, a step-by-step manual network visualization may not be the most efficient way to explore the space of various possibilities for visualizations of a given network. Sometimes, there is more than one layout/property that results in informative plots. Often, the optimal network layout and node/edge properties are not obvious. It often requires not only a decision about the most appropriate network layout, but also a decision about which node/edge properties to select in order to create the most informative network plot: a plot that is worth thousand words. Complexity of network visualization problem becomes more evident in the visualization of large scale networks, as these are often (poorly) visualized as a a big “ hairballs,” preventing users from identifying any underlying network structural patterns.Īs somebody who works with large scale networks, I know how tricky it can be to visualize a network in a comprehensible way. A good network visualization should provide insights into network structural patterns, help identify key nodes and edges, and lead to better understanding of mechanisms of the phenomena it represents. However, network visualization is not a simple problem. , and thus, it is not surprising that network visualization is a hot research problem. Networks are used to describe and model various real-world phenomena such as social relationships or communications, transportation routes, electrical power grids, molecular interactions, etc.
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