1. Social Networking Algorithms related sections to read in Networked Life: 2.1,2.3 3.1 4.1 5.1 6.1-6.2 8.1 9.1

2. The Network Effect Metcalfe's law - the value of a telecommunications network is proportional to the square of the number of connected users of the system (n 2 ) Facebook friends Twitter followers collective opinions on news/products/movies... videos or products or memes going “viral” –if you tell two friends, and they each tell 2 friends...it scales up exponentially to thousands of people in just a few steps

3. Small Worlds phenomenon social networks not same as physical network (because your friends can be remote) also a scale-free topology (power law/Long-tail distribution) 6 degrees-of-separation (Milgram’s exper.) community structure

4. Exploiting the Network Effect Ebay – price discovery through auctions Netflix - recommendations based on others’ preferences Reddit – reputation based on others’ opinions on your posts Crowd-sourcing –is there value in the aggregate opinion? –examples: ratings on Amazon or TripAdvisor or YouTube –combines multiple experts (as well as non-experts) –filters out bias of a few extreme opinions (since you don’t know who to trust)

5. Google Search PageRank algorithm crawling (follow hyperlinks embedded in HTML) >50 billion pages indexed (2012) (not counting intranets) source: http://www.statisticbrain.com/total-number-of-pages-indexed-by-google/ indexing assessing relevance: –number times keyword mentioned –proximity/order –title/heading, bold/fontsize –what makes a page “authoritative”? users only look at top 3-10 hits, so what gets ranked at the top is crucial

6. Inverted Index Basic document retrieval –Build an index of all pages that contain each search term –For multi-word searches, like “functional programming languages”, take intersection of documents with each search term –Does it matter how many times a page mentions a search term? (does this reflect importance? No) –what about dealing with spelling errors, stemming, synonyms, semantic relationships? –more complex Boolean queries (or, not) How do you do this for 50 billion pages? –Google distributes computation over a cluster of computers using MapReduce –programming functions to distribute tasks and assemble results

7. Which search hits are most important? –having many Twitter followers does not make you an expert (populartity ≠ expertise) –similarly, lots of hyperlinks to a page does not mean it is authoritative The web-graph: G=(V,E) –hyperlinks = directed edges –strongly connected components –adjacency matrix (sparse) Joe Student’s Home page. I am a student at Texas A&M I write code in Java Texas A&M Java www.tamu.edu java.sun.com Bowling League Members... Joe

8. PageRank need trust/reputation models? “importance” of a node x i is based on: –importance neighbors who link to you (x J ) –weights 1/d j distribute a node’s importance over the nodes it links to –modify the equations to handle unlinked pages xjxj xixi

9. system of coupled equations –iterative solutions –algorithms that start with random importances and adjust them until all the x i ’s are mutually consistent (convergence) in matrix form, this becomes an eigenvalue problem (hard to calculate) –x is a vector of importances –H is the weighted adjacency matrix x = Hx x1=0.128 x2=0.159 x3=0.202 x4=0.150 x5=0.106 x6=0.044 x7=0.060 x8=0.145