As I said before, the problem of clutter is pervasive in network visualization. Every time you use node link diagrams, you are always running the risk of creating a lot of clutter. So, I think before we move on to other techniques that reduce clutter by design, I think it's important to mention some of the most common ways clutter can be reduced. So, what we have just shown which is edge bundling, but there are other methods. So, one is to use clustering, which is basically trying to group together nodes according to how much they are connected together. Another one is to not drawing the edges. As you may have noticed already, most of the clutter comes from the edges and they're crossing. So, another way to go is to just remove the edges. Finally, there is a thing called node and edge aggregation or simplification. So, the idea is to group nodes and or edges together. Let me show you some examples. So, this is edge bundling, which is exactly what I've just shown you in the previous video. This is an example of clustering, community clustering. So, the graph on the left is just a network graph. It comes from a project called vizster. It visualizes a small social network, small online social network. What you see on the right is exactly the same network with the difference that nodes that are highly connected together have been grouped together through our community detection algorithm, and there are these colored bubbles that contain the nodes that are grouped together. As you can see, it's much much easier to abstract away from the network and focus on some groups. So, this is a very powerful technique. Another technique is as I said, another possibility is to just not draw the edges. So, in this map, you see again an origin destination map where originally only the nodes are drawn, and the edges are only drawn on demand. In this specific case, by going over with your mouse on one specific nodes, then only the nodes that are connected to the selected node are selected. Here is a little bit of a preview of something that is coming up next, which is exactly how to use interaction to deal with some problems that some visualizations have. The last method that I want to mention is aggregation or simplification. This solution that you see here comes from a specific technique called motif simplification created by Cody, Dunne, and Ben Schneiderman. The basic idea is to analyze the graph and try to group together nodes that can be simplified, and group together edges that can be simplified. Let me show you a few examples. So, in the example that you see on the top, there is one node, the green one that is connected to a number of orange nodes and there is nothing else there. So, the idea is why don't we try to group together all of these nodes and replace them with one single mark. Hopefully by doing that, we can reduce the amount of clutter. A very very similar idea is the one that you see at the bottom. So, if we have two nodes, the two green nodes that are connected by several edges, can we just group together all the edges since they all represent the same thing? That's the basic idea. So, this is an example of a very complex graph that you see on the left, and the one that you see on the right is diversion that is created when the nodes and the edges are aggregated using the motif simplification method. So, the basic idea here is if there is a way to reduce the number of nodes and edges, hopefully we can get to a network that is at least as expressive as the initial one but reduces the clutter considerably.