Εισαγωγή στην ανάλυση συνδέσμων
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- Ἴκαρος Βαρουξής
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1 Εισαγωγή στην ανάλυση συνδέσμων Αποθήκες και Εξόρυξη Δεδομένων Διδάσκων: Μαρία Χαλκίδη
2 Why link analysis? Why link analysis? The web is not just a collection of documents its hyperlinks are important! A link from page A to page B may indicate: A is related to B, or A is recommending, citing or endorsing B Links are either referential click here and get back home, or Informational click here to get more detail Αποθήκες και Εξόρυξη Δεδομένων, Παν. Πειραιώς 2
3 Citation Analysis The impact factor of a journal = A/B A is the number of current year citations to articles appearing in the journal during previous two years. B is the number of articles published in the journal during previous two years. Αποθήκες και Εξόρυξη Δεδομένων, Παν. Πειραιώς 3
4 Co-Citation A and B are co-cited by C, implying that they are related or associated. The strength of co-citation between A and B is the number of times they are co-cited. Αποθήκες και Εξόρυξη Δεδομένων, Παν. Πειραιώς 4
5 Clusters from Co-Citation Graph Αποθήκες και Εξόρυξη Δεδομένων, Παν. Πειραιώς 5
6 What is a Markov Chain? A Markov chain has two components: A network structure much like a web site, where each node is called a state. A transition probability of traversing a link given that the chain is in a state. For each state the sum of outgoing probabilities is one. A sequence of steps through the chain is called a random walk. Αποθήκες και Εξόρυξη Δεδομένων, Παν. Πειραιώς 6
7 Markov Chain Example Αποθήκες και Εξόρυξη Δεδομένων, Παν. Πειραιώς 7
8 The Random Surfer Assume the web is a Markov chain. Surfers randomly click on links, where the probability of an outlink from page A is 1/m, where m is the number of outlinks from A. The surfer occasionally gets bored and is teleported to another web page, say B, where B is equally likely to be any page. Using the theory of Markov chains it can be shown that if the surfer follows links for long enough, the PageRank of a web page is the probability that the surfer will visit that page. Αποθήκες και Εξόρυξη Δεδομένων, Παν. Πειραιώς 8
9 Dangling Pages B Problem: A and B have no outlinks. Solution: Assume A and B have links to all web pages with equal probability. Αποθήκες και Εξόρυξη Δεδομένων, Παν. Πειραιώς 9
10 Rank Sink Problem: Pages in a loop accumulate rank but do not distribute it. Solution: Teleportation, i.e. with a certain probability the surfer can jump to any other web page to get out of the loop. Αποθήκες και Εξόρυξη Δεδομένων, Παν. Πειραιώς 10
11 PageRank Overview Out-link from A Dangling link A B C D In-link from B Method to rank web pages giving to it a numeric value that represent their importance Based on the link structure of the web A page X has a high rank if: - It has many In-links or few but highly ranked - Has few Out-links Αποθήκες και Εξόρυξη Δεδομένων, Παν. Πειραιώς 11
12 Google s PageRank Assumption the prestige of a page is proportional to the sum of the prestige scores of pages linking to it Rank of a web page depends on the rank of the web pages pointing to it P is a web page P i are the web pages that have a link to P O(P i ) is the number of outlinks from P i d is the teleportation probability N is the size of the web Αποθήκες και Εξόρυξη Δεδομένων, Παν. Πειραιώς 12
13 Example Web Graph Αποθήκες και Εξόρυξη Δεδομένων, Παν. Πειραιώς 13
14 Iteratively Computing PageRank Replace d/n in the def. of PR(P) by d, so PR will take values between 1 and N. d 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: Αποθήκες και Εξόρυξη Δεδομένων, Παν. Πειραιώς 14
15 Personalised PageRank Change d/n with dv Instead of teleporting uniformly to any page we bias the jump prefer some pages over others. E.g. v has 1 for your home page and 0 otherwise. E.g. v prefers the topics you are interested in. Αποθήκες και Εξόρυξη Δεδομένων, Παν. Πειραιώς 15
16 HITS Hubs and Authorities - Hyperlink-Induced Topic Search A on the left is an authority A on the right is a hub Αποθήκες και Εξόρυξη Δεδομένων, Παν. Πειραιώς 17
17 Hubs and Authorities Key ideas Hubs and authorities are fans and centers in a bipartite core of a web graph A good hub page is one that points to many good authority pages A good authority page is one that is pointed to by many good hub pages Relationship between hubs and authorities helps the mining of authoritative web pages and automated discovery of high-quality web structures and resources Αποθήκες και Εξόρυξη Δεδομένων, Παν. Πειραιώς 18
18 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 in W as to pages outside W. Αποθήκες και Εξόρυξη Δεδομένων, Παν. Πειραιώς 19
19 Pre-processing for HITS Collect the top t pages (say t = 200) based on the input query; call this the root set. Extend the root set into a base set as follows, for all pages p in the root set: add to the root set all pages that p points to, and add to the root set up-to q pages that point to p (say q = 50). Delete all links within the same web site in the base set resulting in a focused sub-graph. Αποθήκες και Εξόρυξη Δεδομένων, Παν. Πειραιώς 20
20 Expanding the Root Set Αποθήκες και Εξόρυξη Δεδομένων, Παν. Πειραιώς 21
21 HITS Algorithm Iterate until Convergence Authority weight Hub weight 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 Αποθήκες και Εξόρυξη Δεδομένων, Παν. Πειραιώς 22
22 HITS Algorithm output HITS outputs a short list of the pages with large hub weights pages with large authority weights for the given search topic Αποθήκες και Εξόρυξη Δεδομένων, Παν. Πειραιώς 23
23 Applications of HITS Search engine querying (speed an issue) Finding web communities. Finding related pages. Populating categories in web directories. Citation analysis. Αποθήκες και Εξόρυξη Δεδομένων, Παν. Πειραιώς 24
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