R Tutorial : Network Attributes

Key Takeaways

within any social network we typically have more information about vertices and edges than whether they exist or not this information may be important when we analyze or visualize the network in the example network from the previous video we already have one vertex attribute each vertex has a label or a name we can see this in the eye graph object where it says a TTR named vertex attributes may be categorical or numerical in friendship or coworker networks other vertex attributes may include the age gender or political affiliation of the individual in a network of flight routes between cities a vertex attribute may be the population of the city or the country that the vertex is in edges may also have attributes this may refer to the type of relationship that individual vertices have for instance it could be whether an interconnection is romantic or platonic in a friendship network or whether a flight route between cities is scheduled daily or weekly the most common edge attribute is the weight of the edge the weight of an edge is visualized by adjusting the relative thickness of edges the thicker the edge the higher the weight of the edge in a friendship network the weight of an edge may refer to how many times in a week friends call each other in a flight route network it may refer to how many flights per week go between two cities to add these four attributes directly to networks that already exist as I graph objects you can use the functions set underscore vertex underscore attribute and set underscore edge underscore attribute the first argument to each is the graph object the second argument should be what you wish to call the new attribute the final argument are the values to include here we are adding a vertex attribute called ages and an edge attribute called frequency you can view vertex and edge attributes using the functions vertex underscore attribute and edge and the score attribute respectively alternatively if you already have all your attributes in data frames then you can create an eye graph object that automatically contains all attributes by using graph underscore from underscore data underscore frame often you may wish to inspect the eye graph object to identify certain vertices or find edges that have some attribute this is possible by sub setting the edges of the eye graph object in the first example we are looking for all edges that include the vertex II using Inc the name of the vertex needs to be in quotes in the second example we subset all edges that have a frequency of greater than or equal to 3 this can be very useful in large networks to identify interesting relationships finally in this section you are going to further develop your eye graph network visualization skills it is possible to adjust basic eye graph plots by adding parameters to the plot function for instance here we create a vertex attribute called color that I graph will use to plot vertex colors we will make all vertices over the age of 22 red and make the remainder white we also specify to add black labels to each vertex using the vertex label color argument now it's your turn