In this webinar, we will explore the ability of Wolfram Language to understand the structure of graphs—in particular, those that are organic and asymmetrical, such as social networks, coauthorship graphs or even the type of "real-life" interaction graph studied by epidemiologists.
We will show a few methods for detecting communities and central nodes in graphs (as well as define what that even means). We will also talk about homophily in social graphs and discuss why it seems like your friends have more friends than you. We will conclude by showing how we can analyze flow networks in Wolfram Language—letting us discover, for example, the peak bandwidth of an intranet or the cheapest way to disrupt a national transport network.