Another important aspect to consider when you are designing network visualizations is what kind of additional attributes you may be able to encode in the network. So, of course, as I said before, there are many variants in terms of layout, but that's not the only set of parameters you can play with. So, in particular, one way to think about it is that the same way in other types of visualizations you can encode some of the additional attributes which may be categories or quantities typically, you can do the same thing with the network and in particular, what you can do is to encode additional information related to the nodes and encode additional information that is associated with the links, the edges. So, more specifically, typically, even if that's not an exhaustive list, what you see, the most common type of encodings are the following. For nodes, you may want to use the node color or node shape when you want to encode some categorical information in the nodes. Another very common solution is to the size of the node to some quantitative attribute that you have associated to the nodes. Whereas, regarding the links, what you typically have is that the thickness of the link is proportional to some quantity that you want to map to the links. Typically, some kind of strength, bit, or volume of something that passes through these nodes. So, for instance, let's say, in a network that shows information about, let say, migratory patterns from one region to another in the world, and the number of people that migrate in one direction is maybe an important element to visualize, and you may want to map this value to the actual volume. You can also map categorical information on the edges, on the links. You don't have a lot of leeway there, but let say, you may want to distinguish between two or three different values and typically, you can use different dashing patterns in the lines to represent this information. So, let's turn these into a practical example. So, let's work again with the Friendship Network. Let's say we have the original Friendship Network, and let's say that we want to map information about the gender of the people who are in this network. Well, how do you visualize that? Well, you can use color or shape. Let's say that now you want to visualize the age of every person. Well, you can visualize that with the node size or even with color using only color intensity, which is the appropriate way of representing a quantity with color. Similarly, if you want to encode information on the edges, let's say that you want to encode information about how many messages in every two pair of people exchanged in Facebook, well, then you can map this information on the thickness of the line. Last, let's say that you want to represent information about whether the connection between two people is old or new according to some parameter. How do you distinguish? Well, maybe you want to use a Tech Store, different types of dashed patterns to distinguish between old and new. So, why do I say that? Well, because I think it's important for you to keep in mind that not only in networks is important, in networks and in force-directed layout is important, the positioning on the nodes, but what may be really important, is useful to let certain patterns emerge, is values that are associated to the nodes and to the edges. I want to conclude this part by talking about an additional important problem that you typically have with some networks. This problem stems from situations where the direction of the connection between two nodes, the direction of the edge is important and needs any piece of information that is contained in your data. As an example, previously, I mentioned the idea of representing migratory patterns as a network. Well, that's a clear example of edges that they have a direction. These are typically called directed graphs. If you want to visualize direction in a network visualization, you have an additional problem. How do you visualize direction? It's a very complex problem, especially when you have lots of nodes and lots of edges. So, clear, it's a typical problem that you have with visualization of directed graphs. How do you do that? Well, there are a number of different visual properties that you can use. Here I'm going to mention some of the available ones. By far, the most common one is to use arrows. But again, the problem is that with arrows, you can get your visualization cluttered very, very easily. So, as soon as you go beyond a few networks, a few nodes and edges, there is a high chance that it's very hard to follow the direction of the lines. Some alternatives are tapered lines. So, lines that they are tapered. So, they clearly show the direction. There are also solutions that use color intensity and/or different color shades. You can also use different types of texts tools and sometimes you can also use animation. So, these are some of the available properties out there. What I'm not going to do is to say which one is better because I think it really, really depends on the particular situation, what kind of data you have, how it's configured, what type of layout you have. So, I think what is really important for you to know is that these options are available and typically, before deciding which one to use, the best way to go is to experiment with all of them.
