With the same file we created above to display the internal PageRank. In Gephi we will go to the our Majestic CF data. In my chart below, you can see which pages are the strongest on the website, taking into account external measures of page strength. Excel Shadow Making adding quote stream to nodes You can tell a lot from this image alone. When you turn on labels, you can see which pages each circle represents. The color indicates which grouping and the size of the circle indicates the relative strength of the page. The further apart these points are, the Shadow Making less the pages are internally linked. You can tell by the number of nodes of each color for which categories the client created the most content and what was successful in attracting external links.
But if you try a few, you'll start to see common issues or maybe even something new and different. These visualizations will allow you to help customers understand Shadow Making the issues you are always talking about. I promise your customers will love them. Gephi offers a number of export options for .png, .svg, or .pdf if you want to create static images. More fun is exporting for use on a web page to create an interactive experience. To do this, check out the Gephi plugins - in particular, the SigmaJS exporter and the Gexf-JS web viewer. What else can we Shadow Making do with Gephi? Add additional link information If you have a crawler that can identify link locations, you can adjust your edge weights differently based on link location. Suppose, for example.
You can always use Gephi, although your chart will probably look more like a star map. I graphed the internal links for Search Engine Land, but had to adjust Shadow Making the scaling to 5000 and the gravity to 0.2 in the ForceAtlas 2 parameter. You can still run calculations for PageRank and Modularity, but you'll probably need to change the node size to something huge to see the data on your chart. You may also need to add more colors to the palette as Shadow Making described earlier because there are likely to be many more distinct modularity classes in a dataset of this size. This is what the SEL graph looks like before coloring. Land Gephi search engine Why is all this important.