Because I decided to add lecture topics on Map-reduce and search advertising, I will have to skip one of the topics I normally cover. I reluctantly decided that it would be "Social Networks"--mostly because it is a self-contained 2-class topic for which video lectures from a previous offering are available (see below).
In addition to being quite engaging, this topic also comes bundled with a discussion on power laws and their importance.
I would encourage you to watch the video lectures. I will also give optional homework questions on the topic. If you choose to do them, you can get extra credit.
Social networks and their applications on the Web
- L14 Audio of [March 5, 2010] (Video of the lecture video part 1 (the first 1hr 5min 4gb) and video part 2 (the remaining 10+ min) Efficient computation of pagerank (and how it is important to not represent the M* matrix explicitly given that it is not sparse); doing block-based pagerank iteration to avoid thrashing, the use of asynchronous pagerank iteration to improve convergence.
[starts at 25min] Social networks start. Connections between link-analysis and the general field of social network analysis. Some iconic examples of social network analysis (Typhoid Mary, Patient Zero, Web graph (and its small-world nature), Offcial florida ballot viral spread, Saddam capture, Aardvark acquisition). Applications of social networks. Graph-based representation and analysis of social entworks. Measures of influence and centrality. Smallworld phenomena--and their examples in kevin bacon game and erdos number.
- L15 Audio of [March 9, 2010] (Video of the lecture video part 1 (the first 1hr 5min 4gb) and video part 2 (the remaining 10+ min) Milgram experiment; six-degrees of separation; (uniform) random networks and their properties; realizing that the small world probability increases sharply to 1 right near k=1; where k is the average (expected) degree of the random network. If human networks are (uniform) random, then they will have small-world phenomena (since k, i.e., average number of friends per person, is almost always greater than 1). Trying to confirm whether large-scale social networks are in fact uniform random by comparing their degree distribution to the Poisson degree distribution expected for random networks. Realizing that most real world network degree distributions instead correspond to negative sloping straightlines in log-log space (which means they are of the form P=1/k^r, which is called powerlaws. Discussion of the properties of power-law disributions (which have long tails that fall off only polynomially rather than exponentially). Implications of long tails on everything from probability of existence of such highly-linked sites as google to the ability of making money selling west wing DVDs and iranian classical music CDs on the web. Discussion of generative models which can result in power law distributions over network degrees.
- L16 Audio of [March 11, 2010] (Video of the lecture video part 1 (the first 1hr; battery died after that :( ) Attacks vs. disruptions on powerlaw vs. exponential networks; navigation on social networks; applications of social networks; discussion of trust and reputation; trust rank (a page-rank variant); discussion of social search (and aardvark); discussion of othe powerlaws in cse494--zipf's law; heap's law (and even benford's law).