In this lecture, we continue with the theme of information spread in networks, turning to the effect of graph topology. In particular, we will discuss influence models for social networks like Facebook, and how to measure importance.

At one time, there were rumors that the Internet has an Achilles' Heel, or a few center points which if attacked would completely disconnect the Internet. In this lecture, we debunk this myth, by showing that the fact the Internet is a "scale-free" network does imply it comes from a network model which would have such center points.

In the last lecture, we studied flat-rate and usage-based pricing schemes for mobile carriers. What these both fail to model is the time varying aspect of demand: consumption varies throughout the day, leading to peaks and troughs in usage. In this lecture, we will look at methods of Smart Data Pricing for taking this into account.

When the demand for capacity on the Internet exceeds the available supply on the network links, we have congestion. In this lecture, we will discuss the principles of distributed congestion control, and will detail protocols that have been designed to regulate demand for the Internet.

The Cloud is another rapidly growing Internet service, allowing users to rent storage and computation resources inside the network. In this lecture, we will see how the large data centers operated by Cloud providers can be constructed from a multitude of small switches.

WiFi hotspots have become an essential feature of our wireless lifestyle. In this lecture, we will study WiFi, and focus specifically on common link layer protocols that are used to manage interference. In doing so, we will see why WiFi does not scale well beyond several devices sharing one access point.

In this final lecture of the course, we will study a subject that we touched upon many times previously and forms an essential part of both social choice theory and technology network design: quantifying fairness of resource allocation.