Hubs and Authorities on the world wide web

Hubs and Authorities on the world wide web (most from Rao’s lecture slides) Presentor: Lei Tang

Desiderata for link-based ranking • • A page that is referenced by lot of important pages (has more back links) is more important (Authority) – A page referenced by a single important page may be more important than that referenced by five unimportant pages – No links between competitive authorities(like Ford, Honda) A page that references a lot of important pages is also important Different (Hub) Notions of Good authoritative pages (authorities) and good hub pages importance (hubs) reinforce each other. “Importance” can be propagated – Your importance is the weighted sum of the importance conferred on you by the pages that refer to you – The importance you confer on a page may be proportional to how many other pages you refer to (cite) • (Also what you say about them when you cite them!)

Authority and Hub Pages (2) • Authorities and hubs related to the same query tend to form a bipartite subgraph of the web graph. hubs authorities • A web page can be a good authority and a good hub.

Authority and Hub Pages (7) Operation I: for each page p: a(p) = q: (q, p) E h(q) q 1 q 2 p q 3 Operation O: for each page p: h(p) = q: (p, q) E a(q) q 1 p q 2 q 3

Authority and Hub Pages (8) Matrix representation of operations I and O. Let A be the adjacency matrix of SG: entry (p, q) is 1 if p has a link to q, else the entry is 0. Let AT be the transpose of A. Let hi be vector of hub scores after i iterations. Let ai be the vector of authority scores after i iterations. Operation I: ai = AT hi-1 Operation O: hi = A ai Normalize after every multiplication

Authority and Hub Pages (11) Example: Initialize all scores to 1. 1 st Iteration: q 1 p 1 I operation: q 2 a(q 1) = 1, a(q 2) = a(q 3) = 0, p 2 a(p 1) = 3, a(p 2) = 2 q 3 O operation: h(q 1) = 5, h(q 2) = 3, h(q 3) = 5, h(p 1) = 1, h(p 2) = 0 Normalization: a(q 1) = 0. 267, a(q 2) = a(q 3) = 0, a(p 1) = 0. 802, a(p 2) = 0. 535, h(q 1) = 0. 645, h(q 2) = 0. 387, h(q 3) = 0. 645, h(p 1) = 0. 129, h(p 2) =0

Authority and Hub Pages (12) After 2 Iterations: a(q 1) = 0. 061, a(q 2) = a(q 3) = 0, a(p 1) = 0. 791, a(p 2) = 0. 609, h(q 1) = 0. 656, h(q 2) = 0. 371, h(q 3) = 0. 656, h(p 1) = 0. 029, h(p 2) = 0 After 5 Iterations: q 1 p 1 a(q 1) = a(q 2) = a(q 3) = 0, q 2 p 2 a(p 1) = 0. 788, a(p 2) = 0. 615 q 3 h(q 1) = 0. 657, h(q 2) = 0. 369, h(q 3) = 0. 657, h(p 1) = h(p 2) = 0

(why) Does the procedure converge? x x 2 xk As we multiply repeatedly with M, the component of x in the direction of principal eigen vector gets stretched wrt to other directions. . So we converge finally to the direction of principal eigenvector Necessary condition: x must have a component in the direction of principal eigen vector (c 1 must be non-zero) The rate of convergence depends on the “eigen gap”

Authority and Hub Pages (3) Main steps of the algorithm for finding good authorities and hubs related to a query q. 1. Submit q to a regular similarity-based search engine. Let S be the set of top n pages returned by the search engine. (S is called the root set and n is often in the low hundreds). 2. Expand S into a large set T (base set): • Add pages that are pointed to by any page in S. • Add pages that point to any page in S. • If a page has too many parent pages, only the first k parent pages will be used for some k.

Authority and Hub Pages (4) 3. Find the subgraph SG of the web graph that is induced by T. T S

Authority and Hub Pages (5) Steps 2 and 3 can be made easy by storing the link structure of the Web in advance Link structure table (during crawling) --Most search engines serve this information now. (e. g. Google’s link: search) parent_url 1 child_url 2 url 3

Authority and Hub Pages (6) 4. Compute the authority score and hub score of each web page in T based on the subgraph SG(V, E). Given a page p, let a(p) be the authority score of p h(p) be the hub score of p (p, q) be a directed edge in E from p to q. Two basic operations: • Operation I: Update each a(p) as the sum of all the hub scores of web pages that point to p. • Operation O: Update each h(p) as the sum of all the authority scores of web pages pointed to by p.

Authority and Hub Pages (9) After each iteration of applying Operations I and O, normalize all authority and hub scores. Repeat until the scores for each page converge (the convergence is guaranteed). 5. Sort pages in descending authority scores. 6. Display the top authority pages.

Authority and Hub Pages (10) Algorithm (summary) submit q to a search engine to obtain the root set S; expand S into the base set T; obtain the induced subgraph SG(V, E) using T; initialize a(p) = h(p) = 1 for all p in V; for each p in V until the scores converge { apply Operation I; apply Operation O; normalize a(p) and h(p); } return pages with top authority scores;

Handling “spam” links Should all links be equally treated? Two considerations: • Some links may be more meaningful/important than other links. • Web site creators may trick the system to make their pages more authoritative by adding dummy pages pointing to their cover pages (spamming).

Handling Spam Links (contd) • Transverse link: links between pages with different domain names. Domain name: the first level of the URL of a page. • Intrinsic link: links between pages with the same domain name. Transverse links are more important than intrinsic links. Two ways to incorporate this: 1. Use only transverse links and discard intrinsic links. 2. Give lower weights to intrinsic links.

Handling Spam Links (contd) How to give lower weights to intrinsic links? In adjacency matrix A, entry (p, q) should be assigned as follows: • If p has a transverse link to q, the entry is 1. • If p has an intrinsic link to q, the entry is c, where 0 < c < 1. • If p has no link to q, the entry is 0.

Considering link “context” For a given link (p, q), let V(p, q) be the vicinity (e. g. , 50 characters) of the link. • If V(p, q) contains terms in the user query (topic), then the link should be more useful for identifying authoritative pages. • To incorporate this: In adjacency matrix A, make the weight associated with link (p, q) to be 1+n(p, q), • • where n(p, q) is the number of terms in V(p, q) that appear in the query. Alternately, consider the “vector similarity” between V(p, q) and the query Q

Evaluation Sample experiments: • Rank based on large in-degree (or backlinks) query: game Rank in-degree URL 1 13 http: //www. gotm. org 2 12 http: //www. gamezero. com/team-0/ 3 12 http: //ngp. ngpc. state. ne. us/gp. html 4 12 http: //www. ben 2. ucla. edu/~permadi/ gamelink/gamelink. html 5 11 http: //igolfto. net/ 6 11 http: //www. eduplace. com/geo/indexhi. html • Only pages 1, 2 and 4 are authoritative game pages.

Evaluation Sample experiments (continued) • Rank based on large authority score. query: game Rank Authority 1 0. 613 2 0. 390 3 4 5 6 0. 342 0. 324 0. 306 URL http: //www. gotm. org http: //ad/doubleclick/net/jump/ gamefan-network. com/ http: //www. d 2 realm. com/ http: //www. counter-strike. net http: //tech-base. com/ http: //www. e 3 zone. com • All pages are authoritative game pages.

Authority and Hub Pages (19) Sample experiments (continued) • Rank based on large authority score. query: free email Rank Authority URL 1 0. 525 http: //mail. chek. com/ 2 0. 345 http: //www. hotmail/com/ 3 0. 309 http: //www. naplesnews. net/ 4 0. 261 http: //www. 11 mail. com/ 5 0. 254 http: //www. dwp. net/ 6 0. 246 http: //www. wptamail. com/ • All pages are authoritative free email pages.

Tyranny of Majority Which do you think are Authoritative pages? Which are good hubs? -intutively, we would say that 4, 8, 5 will be authoritative pages and 1, 2, 3, 6, 7 will be hub pages. 1 2 3 4 5 6 7 8 ss ma y ub l d h plete , as n m a ity te co onent next The power iteration will show that author tra omp (See he oncen first c ease. T lc Only 4 and 5 have non-zero authorities il g the s incr W on n [. 923. 382] m teratio A i And only 1, 2 and 3 have non-zero hubs The e) [. 5. 7. 5] slid BUT

Tyranny of Majority (explained) Suppose h 0 and a 0 are all initialized to 1 m p 1 p 2 pm p n m>n q 1 qn q

Impact of Bridges. . 9 1 When the graph is disconnected, only 4 and 5 have non-zero authorities [. 923. 382] And only 1, 2 and 3 have non-zero hubs [. 5. 7. 5]CV 2 3 4 5 6 7 8 When the components are bridged by adding one page (9) the authorities change w only 4, 5 and 8 have non-zero authorities m f vie fro t o [. 853. 224. 47] s ew poin And 1, 2, 3, 6, 7 and 9 will have non-zero hubs d n lity Ba bi [. 39. 49. 39. 21. 6] sta

Authority and Hub Pages (24) Multiple Communities (continued) • How to retrieve pages from smaller communities? A method for finding pages in nth largest community: – Identify the next largest community using the existing algorithm. – Destroy this community by removing links associated with pages having large authorities. – Reset all authority and hub values back to 1 and calculate all authority and hub values again. – Repeat the above n 1 times and the next largest community will be the nth largest community.

Multiple Clusters on “House” Query: House (first community)

Authority and Hub Pages (26) Query: House (second community)

Can be done For base set too Can be done Query relevance vs. query time co For mputation tradeoff full web too See topic-specific Page-rank idea. . More stable because random surfer model allows low prob edges to every place. CV Can be made stable with subspace-based A/H values [see Ng. et al. ; 2001]

Novel uses of Link Analysis • Link analysis algorithms—HITS, and Pagerank—are not limited to hyperlinks - Citeseer/Cora use them for analyzing citations (the link is through “citation”) - See the irony here—link analysis ideas originated from citation analysis, and are now being applied for citation analysis - Some new work on “keyword search on databases” uses foreign-key links and link analysis to decide which of the tuples matching the keyword query are most important (the link is through foreign keys) - [Sudarshan et. Al. ICDE 2002] - Keyword search on databases is useful to make structured databases accessible to naïve users who don’t know structured languages (such as SQL).