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. 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.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).| | |
| | |
| | |
| 88.40 | A+ |
| 79.57 | A |
| 62.83 | B |
| | |
| 91.97 | A+ |
| 90.61 | A+ |
| 87.55 | A+ |
| 85.76 | A |
| 85.11 | A |
| 84.34 | A |
| 84.00 | A |
| 83.88 | A |
| 83.85 | A |
| 81.27 | A- |
| 79.92 | B+ |
| 78.76 | B+ |
| 78.63 | B+ |
| 74.76 | B |
| 69.64 | B |
| 69.35 | B |
