This lecture discusses the structure of the web, link analysis, and web search. It covers the basic components of a search engine including crawling, indexing, ranking, and query processing. It describes how web crawlers work by recursively fetching links from seed URLs. It also discusses link-based ranking algorithms like PageRank that rank pages based on the link structure of the web. The lecture further covers challenges like spam and approaches to detect web spam like TrustRank, Anti-TrustRank, Spam Mass, and Link Farm spam. The author proposes techniques to refine seed sets and order algorithms to improve web spam filtering.

1. CSE509: Introduction to Web Science and Technology Lecture 3: The Structure of the Web, Link Analysis and Web Search Muhammad AtifQureshi and ArjumandYounus Web Science Research Group Institute of Business Administration (IBA)

2. Last Time… Basic Information Retrieval Approaches Bag of Words Assumption Information Retrieval Models Boolean model Vector-space model Topic/Language models July 23, 2011

3. Today Search Engine Architecture Overview of Web Crawling Web Link Structure Ranking Problem SEO and Web Spam Web Spam Research July 23, 2011

4. Introduction World Wide Web has evolved from a handful of pages to billions of pages In January 2008, Google reported indexing 30 billion pages and Yahoo 37 billion. In this huge amount of data, search engines play a significant role in finding the needed information Search engines consist of the following basic operations Web crawling Ranking Keyword extraction Query processing July 23, 2011

5. General Architecture of a Web Search Engine Web User Query Crawler Indexing Visual Interface Index Ranking Query Operations July 23, 2011

6. CRAWLING MODULE July 23, 2011

7. Web Crawler Definition Program that collects Web pages by recursively fetching links (i.e., URLs) starting from a set of seed pages [HN99] Objective Acquisition of large collections of Web pages to be indexed by the search engine for efficient execution of user queries July 23, 2011 Introduction

8. Basic Crawler Operation Place known seed URLs in the URL queue Repeat following steps until a threshold number of pages downloaded Fetch a URL on the URL queue and download the corresponding Web page For each downloaded Web page Extract URLs from the Web page For each extracted URL, check validity and availability of URL using checking modules Place the URLs that pass the checks on the URL queue July 23, 2011 New URLs Seed URLs NOTATIONS USED : queue : module : data flow URLs to crawl Web pages Checking module Extracted URLs URL duplication check Web page downloader URL queue Link extractor DNS resolver Crawled Web pages URLs to crawl Robots check Web

9. Crawling Issues Load at visited Web sites Load at crawler Scope of crawl Incremental crawling July 23, 2011

10. RANKING MODULE July 23, 2011

11. Problems of TFIDF Vector Works well on small controlled corpus, but not on the Web Top result for “American Airlines” query: accident report of American Airline flights Do users really care how many times American Airlines mentioned? Easy to spam Ranking purely based on page content Authors can manipulate page content to get high ranking Any idea? July 23, 2011

12. Web Page Ranking Motivation User queries return huge amount of relevant web pages, but the users want to browse the most important ones Note: Relevancerepresents that a web page matches the user’s query Concept Ordering the relevant web pages according to their importance Note: Importance represents the interest of a user on the relevant web pages Methods Link-based method: exploiting the link structure of web for ordering the search results Content-based method: exploiting the contents of web pages for ordering the search results July 23, 2011

14. Outlink: the outgoing link from a web node.V = {A, B, C} E = {AB, BC} AB is an outlink of the web node A. BC is an outlink of the web node B. AB is an inlink of the web node B. BC is an inlink of the web node C. B C A Fig. 1: An example of a web graph. July 23, 2011

15. PageRank: Basic Idea Think of …. People as pages Recommendations as links Therefore, “Pages are popular, if popular pages link them” “PageRank is a global ranking of all Web pages regardless of their content, based solely on their location in the Web’s graph structure” [Page et al 1998] July 23, 2011

16. PageRank Overview A web page is more important if it is pointed by many other important web pages The importance of a web page (called PageRank value) represents the probability that a user visits the web page Function July 23, 2011 web page important web page D link C E random jump from F to B A user F B jump to a random page < User’s behavior on the web graph > PR[p]: PageRank value of web page p Nolink(q): number of outlinks of web page q d: damping factor (probability of following a link) v[p]: probability that a user randomly jumps to web page p (random jump value over web page p)

17. PageRank Example July 23, 2011 1 2 3 4

18. PageRank: Problems on the Real Web Dangling nodes A page with no links to send importance All importance “leak out of” the Web Solution: Random surfer model Crawler trap A group of one or more pages that have no links out of the group Accumulate all the importance of the Web Solution: Damping factor July 23, 2011

19. Link Analysis in Modern Web Search PageRank like ideas play basic role in the ranking functions of Google, Yahoo! And Bing Current ranking functions far from pure PageRank Far more complex Evolve all the time Kept in secret! July 23, 2011

20. Search Engine Optimization Important game-theoretic principle: the world reacts and adapts to the rules Web page authors create their Web pages with the search engine’s ranking formula in mind July 23, 2011

21. A Huge Challenge for Today’s Search Engines SEO gives birth to nuisance of Web spam July 23, 2011

22. Web Spam Concept Any deliberate action in order to boost a web node’s rank, without improving its real merit. Link spam: web spam against link-based methods An action that changes the link structure of web in order to boost web node's ranking. Example N1 N2 I want to boost the rank of the web node N3 The web nodes N1and N2 are not involved in link spam, so they care called non-spam nodes N4 Actor creates the web node N3 to Nx N3 N5 Web nodes N3-Nx are involved in link spam, so they are called spam nodes … Nx Node Link Actor Fig. 2: An example of link spam. July 23, 2011

23. TrustRank Overview [GGP04] Trusted domains(e.g., well-known non-spam domains such as .gov and .edu) usually point to non-spam domains by using outlinks. Trust scores are propagated through the outlinks of trusted domains. Domains having high trust scores(≥threshold) at the end of propagation are declared as non-spam domains. Example Observation Trust scores can propagate to spam domains if trusted domain outlinks to the spam domains. 1/2 A domain being considered 5/12 1 1/2 3 5/12 t(1)=1 A seed non-spam domain 1/3 t(3)=5/6 1/3 t(i): The trust score of domain i 2 4 t(2)=1 The domain 3 gets trust scores from the domains 1 and 2. 1/3 t(4)=1/3 Fig. 3: An example for explaining TrustRank. July 23, 2011

24. Anti-TrustRank Overview [KR06] Anti-trusted domains (e.g., well-known spam domains) are usually pointed by spam domains by using inlinks. Anti-trust scores are propagated by the inlinks of anti-trusted domains. Domains having high anti-trust scores(≥threshold) at the end of propagation are declared as spam domains. Example Observation Anti-trust score can propagate to non-spam domains if a non-spam domain outlinks to spam domain. 1/2 A domain being considered 5/12 1 1/2 A seed spam domain 3 5/12 at(1)=1 1/3 at(3)=5/6 at(i): The anti-trust score of domain i 2 1/3 4 The domain 3 gets anti-trust scores from the domains 1 and 2. at(2)=1 at(4)=1/3 1/3 Fig. 4: An example for explaining Anti-TrustRank. July 23, 2011

25. Spam Mass Overview [GBG06] A domain is spam if it has excessively high spam score. Spam score is estimated as subtraction from a PageRank score to a non-spam score. Non-spam score is estimated as a trust score computed by TrustRank. Example Observation Since the Spam Mass has use TrustRank, it has inherently the same problem as TrustRank does. 1 A domain being considered 2 7 5 6 A seed non-spam domain 3 4 Fig. 5: An example for explaining Spam Mass. The domain 5 receives many inlinks but only one indirect inlink from a non-spam domain. July 23, 2011

26. Link Farm Spam Overview[WD05] A domain is spam if it has many bidirectional links with domains. A domain is spam if it has many outlinks pointing to spam domains. Example Observation Link Farm Spam does not take any input seed set. A domain can have many bidirectional links with trusted domains as well. 2 1 3 A domain being considered 4 5 The domains 1, 3, and 4 have two directional links. Fig. 6: An example for explaining Link Farm Spam. July 23, 2011

27. RESEARCH SECTION July 23, 2011

28. Web Spam Filtering Algorithm Overview The web spam filtering algorithms output spam nodes to be filtered out [GBG06]. In order to identify spam nodes, a web spam filtering algorithm needs spam or non-spam nodes (called input seed sets) as an input [GGP04, KR06, GBG06, WD05]. Spam input seed set: the input seed set containing spam nodes. Non-spam input seed set: the input seed set containing non-spam nodes. The input seed set can be used as the basis for grading the degree of whether web nodes are spam or non-spam nodes [GGP04, KR06, GBG06]. Observation The output quality of web spam filtering algorithms is dependent on that of the input seed sets. The output of the one web spam filtering algorithm can be used as the input of the other web spam filtering algorithm.  The algorithms may support one another if placed in appropriate succession. July 23, 2011

29. Motivation and Goal Motivation There is no well-known study which addresses the refinement of the input seed sets for web spam filtering algorithms. There is no well-known study on successions among web spam filtering algorithms. Goal Improving the quality of web spam filtering by using seed refinement. Improving the quality of web spam filtering by finding the appropriate succession among web spam filtering algorithms. July 23, 2011

30. Contributions We propose modified algorithms that apply seed refinement techniques using both spam and non-spam input seed sets to well-known web spam filtering algorithms. We propose a strategy that makes the best succession of the modified algorithms. We conduct extensive experiments in order to show quality improvement for our work. We compare the original(i.e., well-known) algorithms with the respective modified algorithms. We evaluate the best succession among our modified algorithms. July 23, 2011

31. Web Spam Filtering Using Seed Refinement Objectives Decrease the number of domains incorrectly detected as belonging to the class of non-spam domains (called False Positives). Increase the number of domains correctly detected as belonging to the class of spam domains (called True Positives). Our approaches We modify the spam filtering algorithms by using both spam and non-spam domains in order to decrease False Positives. We use non-spam domains so that their goodness should not propagate to spam domains. We use spam domains so that their badness should not propagate to non-spam domains. We make the succession of these algorithms in order to increase True Positives. We make the succession of the seed refinement algorithm followed by the spam detection algorithm so that the spam detection algorithm uses the refined input seed sets, which is produced by the seed refinement algorithm. July 23, 2011

32. Modified TrustRank Modification Trust score should not propagate to spam domains. Example 5/12 1/2 A seed spam domain 5/12 5 6 1 1/2 A domain being considered 3 t(6)=5/12 + … t(5)=5/12 + … t(1)=1 5/12 t(3)=5/6 1/3 A seed non-spam domain 1/3 2 5/12 t(i): The trust score of domain i 4 t(2)=1 The domains 5 and 6 are involved in Web spam. 1/3 t(4)=1/3 Fig. 7: An example explaining Modified TrustRank. July 23, 2011

33. Modified Anti-TrustRank Modification Anti-Trust score should not propagate to non-spam domains. Example 5/12 A seed spam domain at(5)=5/12 1/2 3 1 5 A domain being considered 7 5/12 1/2 5/12 5/12 at(1)=1 at(3)=5/6 5/12 6 at(7)=5/12 + … A seed non-spam domain 1/3 4 at(6)=5/12 + … 2 1/3 at(i): The anti-trust score of domain i at(2)=1 at(4)=1/3 1/3 The domains 5 ,6 and 7 are non- spam domains. Fig. 8: An example explaining Modified Anti-TrustRank. July 23, 2011

34. Modified Spam Mass Modification Use modified TrustRank in place of TrustRank. Example A seed spam domain 1 A domain being considered 2 5 7 6 A seed non-spam domain 3 4 The domain 5 receives many inlinks but only one indirect inlink from a non-spam domain. Fig. 9: An example explaining Modified Spam Mass. July 23, 2011

35. Modified Link Farm Spam Modification Use two types (i.e., spam and non-spam domain) of input seed sets. A domain having many bidirectional links with only trusted domains is not detected as a spam domain. Example 6 8 2 7 A seed non-spam domain 1 3 A domain being considered 4 5 The domains 1, 3, and 4 have two directional links. Fig. 10: An example explaining Modified Link Farm Spam. July 23, 2011

37. Consideration of the execution order in Seed Refiner.

38. Modified TrustRank followed by Modified Anti-TrustRank.

39. Modified Anti-TrustRank followed by Modified TrustRank.

40. Consideration of the execution order in Spam Detector.

41. Modified Spam Mass followed by Modified Link Farm Spam.

42. Modified Link Farm Spam followed by Modified Spam Mass.Manually labeled spam and non-spam domains Seed Refiner Refined spam and non-spam domains Spam Detector Detected spam domains Class Data flow Fig. 11: The strategy of succession. July 23, 2011

43. Performance Evaluation Purpose Show the effect of seed refinement on the quality of web spam filtering. Show the effect of succession on the quality of web spam filtering. Experiments We conduct two sets of the experiments according to the two purposes as mentioned above. Table. 1: Summary of the experiments. July 23, 2011

44. Experimental Parameters Table. 2: Parameters used in experiments. July 23, 2011

46. Experimental Measure Table. 5: Description of the measures. 1False negatives are the number of domains incorrectly labeled as not belonging to the class (i.e., spam or non-spam). July 23, 2011

48. MTR performs either comparable to or slightly better than TR in terms of both true positives and false positives.

50. MATR generally performs better than ATR in terms of true positives

51. We find cutoffATreffective till 180% mark indicating that after 100% detection becomes unstable in terms of false positives.  For later experiments, we fix the cutoffATr at 100% to ensure high precision. July 23, 2011

52. Comparison between Originaland Modified Algorithms (2/3) Experiment 3: Comparison Between SM and MSM MSM performs slightly better than SM in terms of true positives and comparable in terms of false positives We find relativeMasseffective between the range of 0.95 to 0.99 in terms of maximizing true positives and minimizing false positives.  For later experiments, we keep the range from 0.8 to 0.99 of relativeMass as effective range. Experiment 4: Comparison Between LFS and MLFS MLFS performs better than LFS in terms of false positives while at some expense of true positives. We find limitBL and limitOL highly effective at 7 and 7 respectively in terms of minimizing many false positives.  For later experiments, we keep limitBL = 7 and limitOL = 7. July 23, 2011

53. Comparison between Originaland Modified Algorithms (3/3) Summary We have found all modified algorithms providing better quality than the respective original algorithms. We found SM as the best original web spam detection algorithms among ATR, SM, and LFS algorithms due to high true positives and relatively less false positives. We also found MSM as the best modified web spam detection algorithms among MATR, MSM, and MLFS algorithms due to high true positives and relatively less false positives. July 23, 2011

54. The Best Succession for the Seed Refiner Identical performance for both successions Identical performance for both successions Identical performance for both successions Better performance for MATR-MTR compared toMTR-MATR Table. 6: Comparison for the seed refiner. Therefore, MATR-MTR is found to be the winner, and hence we select it as the seed refiner. July 23, 2011

55. The Best Successionfor the Spam Detector Comparison We pick 0.99 of relativeMass since false positives are minimum at this value compared to other values of relativeMass while true positives are almost comparable for all values of relativeMass. We observe MLFS fails to detect considerable number of spam domains. We obtain the precisions 0.86, 0.86, 0.93, and 0.87 for MLFS-MSM, MSM-MLFS, MLFS, and MSM respectively. We obtain the recalls 0.80, 0.80, 0.33, and 0.76 for MLFS-MSM, MSM-MLFS, MLFS, and MSM respectively. MLFS-MSM and MSM-MLFS are best and identical in performance, we choose MLFS-MSM as the best spam detector without loss of generality. Fig. 12: Comparison for the spam detector. July 23, 2011

56. Comparison We pick 0.99 of relativeMass since false positives are minimum at this value compared to other values of relativeMass while true positives are almost comparable for all values of relativeMass. We observe MATR-MTR-MLFS-MSM finds more true positives and some more false positives. We obtain the precisions 0.85, 0.86, and 0.86 for SM, MSM, and MATR-MTR-LFS-MSM respectively. We obtain the recalls 0.64, 0.70, and 0.80 for SM, MSM, and MATR-MTR-LFS-MSM respectively. Comparison among the Best Succession, theBest Known Algorithm and the Best Modified Algorithm Fig. 13:Comparison among MATR-MTR-MLFS-MSM, SM, and MSM. Therefore, MATR-MTR-MLFS-MSM is more effective. July 23, 2011

57. Conclusions We have improved the quality of web spam filtering by using seed refinement We have proposed modifications in four well-known web spam filtering algorithms. We have proposed a strategy of succession of modified algorithms Seed Refiner contains order of executions for seed refinement algorithms. Spam Detector contains order of executions for spam detection algorithms. We have conducted extensive experiments in order to show the effect of seed refinement on the quality of web spam filtering We find that every modified algorithm performs better than the respective original algorithm. We find the best performance among the successions by MATR followed by MTR, MLFS, and MSM (i.e., MATR-MTR-MSM). This succession outperforms the best original algorithm i.e., SM, by up to 1.25 times in recall and is comparable in terms of precision. July 23, 2011

58. QUESTIONS? July 23, 2011