1. CS349 – Link Analysis 1. Anchor text 2. Link analysis for ranking 2.1 Pagerank 2.2 Pagerank variants 2.3 HITS

2. The Web as a Directed Graph Assumption 1: A hyperlink between pages denotes author perceived relevance (quality signal) Assumption 2: The anchor of the hyperlink describes the target page (textual context) Page A hyperlink Page B Anchor

3. Anchor Text For ibm how to distinguish between: IBM’s home page (mostly graphical) IBM’s copyright page (high term freq. for ‘ibm’) Rival’s spam page (arbitrarily high term freq.) www.ibm.com “ibm” “ibm.com” “IBM home page” A million pieces of anchor text with “ibm” send a strong signal

4. Indexing anchor text When indexing a document D, include anchor text from links pointing to D. www.ibm.com Armonk, NY-based computer giant IBM announced today Joe’s computer hardware links Compaq HP IBM Big Blue today announced record profits for the quarter

5. Indexing anchor text Can sometimes have unexpected side effects – e.g., evil empire. Can index anchor text with less weight.

6. 2. Citation Analysis Citation frequency The kind of background work Deans are doing at tenure time Co-citation coupling frequency Co-citations with a given author measures “impact” Are you co-cited with influential publications? Bibliographic coupling frequency Articles that co-cite the same articles are related Citation indexing Who is author cited by? (Garfield [Garf72] )

7. PageRank PR – Single-page Definition W is a web page Wi are the web pages that have a link to P O(Wi) is the number of outlinks from Pi t is the teleportation probability N is the size of the web

8. Iteratively Computing PageRanks t is normally set to 0.15, but for simplicity lets set it to 0.5 Set initial PR values to 1 Solve the following equations iteratively:

9. Example Computation of PR

10. Pagerank – Matrix Mult. Definition Imagine a browser doing a random walk on web pages: Start at a random page At each step, go equi-probably out of the current page along one of the links on that page, “In the steady state” each page has a long-term visit rate: Use this rate as the page’s score. 1/3

11. Not quite enough The web is full of dead-ends. Random walk can get stuck in dead-ends. Makes no sense to talk about long-term visit rates. ??

12. Teleporting At a dead end, jump to a random web page. At any non-dead end, With probability, say, 10%, jump to a random web page. With remaining probability (90%), go out on a random link. t=10% is the “teleporting” parameter.

13. Result of teleporting Now cannot get stuck locally. There is a long-term rate at which any page is visited This not obvious, needs proof! How do we compute this visit rate?

14. Markov chains A Markov chain consists of n states, and an n  n transition probability matrix P. At each step, we are in exactly one of the states. For 1  i,j  n, the matrix entry P ij tells us the probability of j being the next state, given we are currently in state i. ij P ij P ii >0 is OK

15. Markov chains Clearly, for all i, Markov chains are abstractions of random walks. Exercise: Represent the teleporting random walk with teleporting parameter t=50% as a Markov chain, for this graph: ABCD

16. Computing P with teleporting t Start with Adjacency matrix A If there is hyperlink from i to j, A ij = 1, else A ij = 0 If a row has all 0’s,  replace each element by 1/N Else  divide each 1 by the number of 1’s in the row  Multiply the matrix by 1-t  Add t/N to every entry of the resulting matrix ABCD

17. Ergodic Markov chains A Markov chain is ergodic if you have a path from any state to any other For any start state, after a finite transient time T 0, the probability of being in any state at time T>T 0 is nonzero. Not ergodic (even/odd).

18. Ergodic Markov chains From Probability theory: For any ergodic Markov chain, there is a unique long-term visit rate for each state. “Steady-state probability distribution” Over a long time-period, we visit each state in proportion to this rate. It doesn’t matter where we start! As long as you walk “long enough”

19. Probability vectors A probability (row) vector x = (x 1, … x n ) tells us where the walk is at any point. E.g., (0 0 0 … 1 … 0 0 0) means we’re in state i. E.g., (0 0 0 … 0.5 … 0.5 … 0 0) means we’re? ___________ in1 More generally, the vector x = (x 1, … x n ) means the walk is in state i with probability x i. in1 j

20. Change in probability vector If the probability vector is x = (x 1, … x n ) at this step, what is it at the next step? Recall that row i of the transition prob. Matrix P tells us where we go next from state i. So from x, our next state is distributed as xP.

21. Steady state example The steady state looks like a vector of probabilities a = (a 1, … a n ): a i is the probability that we are in state i. 12 3/4 1/4 3/41/4 For this example, a 1 =1/4 and a 2 =3/4.

22. How do we compute this vector? Let a = (a 1, … a n ) denote the row vector of steady-state probabilities. If we our current position is described by a, then the next step is distributed as aP. But a is the steady state, so a=aP. Solving this matrix equation gives us a. Linear Algebra anyone? So a is the (left) eigenvector for P. Corresponds to the “principal” eigenvector of P with the largest eigenvalue. Transition probability matrices always have largest eigenvalue.

23. One way of computing a (all Pageranks) Recall, regardless of where we start, we eventually reach the steady state a. Start with any distribution (say x=(1 0 … 0)). After one step, we’re at xP; after two steps at xP 2, then xP 3 and so on. “Eventually” means for “large” k, xP k = a. Algorithm: multiply x by increasing powers of P until the product looks stable.

24. Pagerank summary Preprocessing: Given graph of links, build matrix P. From it compute a. The entry a i is a number between 0 and 1: the pagerank of page i. Query processing: Retrieve pages meeting query. Rank them by their pagerank. Order is query-independent If PR(A) > PR(B) for some query, it beats it in every query

25. How is Pagerank used? PageRank Technology: PageRank reflects our view of the importance of web pages by considering more than 500 million variables and 2 billion terms. Pages that we believe are important pages receive a higher PageRank and are more likely to appear at the top of the search results. http://www.google.com/corporate/tech.html “Pagerank is used in Google, but so are many other clever heuristics.” Pagerank is dead, long live Pagerank!

26. Extended PageRank example

29. 2.2 Pagerank: Issues and Variants How realistic is the random surfer model? What if we modeled the back button? [Fagi00] Surfer behavior sharply skewed towards short paths [Hube98] Search engines, bookmarks & directories make jumps non- random. Biased Surfer Models Weight edge traversal probabilities based on match with topic/query (non-uniform edge selection) Bias jumps to pages on topic (e.g., based on personal bookmarks & categories of interest)

30. Topic Specific Pagerank [Have02] Conceptually, we use a random surfer who teleports, with say 10% probability, using the following rule:  Selects a category (say, one of the 16 top level ODP categories) based on a query & user -specific distribution over the categories  Teleport to a page uniformly at random within the chosen category Sounds hard to implement: can’t compute PageRank at query time!

31. Implementation of Topic Specific Pagerank offline: Compute pagerank distributions w.r.t. individual categories Query independent model as before Each page has multiple pagerank scores – one for each ODP category, with teleportation only to that category online: Distribution of weights over categories computed by query context classification Generate a dynamic pagerank score for each page - weighted sum of category-specific pageranks

32. Influencing PageRank (“Personalization”) Input: Web graph W influence vector v v : (page  degree of influence) Output: Rank vector r: (page  page importance wrt v) r = PR(W, v)

33. Non-uniform Teleportation Teleport with 10% probability to a Sports page Sports

34. Interpretation of Composite Score For a set of personalization vectors {v j }  j [w j · PR(W, v j )] = PR(W,  j [w j · v j ]) Weighted sum of rank vectors itself forms a valid rank vector, because PR() is linear wrt v j

35. Interpretation 10% Sports teleportation Sports

36. Interpretation Health 10% Health teleportation

37. Interpretation Sports Health pr = (0.9 PR sports + 0.1 PR health ) gives you: 9% sports teleportation, 1% health teleportation

38. 3. Hyperlink-Induced Topic Search (HITS) [Klei98] In response to a query, instead of an ordered list of pages each meeting the query, find two sets of inter-related pages: Hub pages are good lists of links on a subject.  e.g., “Bob’s list of cancer-related links.” Authority pages occur recurrently on good hubs for the subject. Best suited for “broad topic” queries rather than for page-finding queries. Gets at a broader slice of common opinion.

39. Pre-processing for HITS 1) Collect the top t pages (say t = 200) based on the input query; call this the root set. 2) Extend the root set into a base set as follows, for all pages p in the root set: 1) add to the root set all pages that p points to, and 2) add to the root set up-to q pages that point to p (say q = 50). 3) Delete all links within the same web site in the base set resulting in a focused sub-graph.

40. Expanding the Root Set

41. HITS Algorithm – Iterate until Convergence B is the base set q and p are web pages in B A(p) is the authority score for p H(p) is the hub score for p

42. Applications of HITS Search engine querying (speed is an issue). Finding web communities. Finding related pages. Populating categories in web directories. Citation analysis.

43. Communities on the Web A densely linked focused sub-graph of hubs and authorities is called a community. Over 100,000 emerging web communities have been discovered from a web crawl (a process called trawling). Alternatively, a community is a set of web pages W having at least as many links to pages inside W as to pages outside W.

44. Weblogs influence on PageRank A weblog (or blog) is a frequently updated web site on a particular topic, made up of entries in reverse chronological order. Blogs are a rich source of links, and therefore their links influence PageRank. A “google bomb” is an attempt to influence the ranking of a web page for a given phrase by adding links to the page with the phrase as its anchor text.

45. Link Spamming to Improve PageRank Spam is the act of trying unfairly to gain a high ranking on a search engine for a web page without improving the user experience. Link farms - join the farm by copying a hub page which links to all members. Selling links from sites with high PageRank.