1. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.1 Chapter 9 : Social Networks What is a social network? Collaboration graphs. The social web. Social network analysis. Web communities P2P networks. Collaborative filtering. Weblogs. Power law distributions on the web. Small-world networks. Searching in social networks.

2. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.2 What is a Social Network? A network between people, e.g.: –A network of acquaintances. –A collaboration network between academics –A network of film actors. –An email network. –A network of personal web sites.

3. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.3 Collaboration Graphs E.g. between researchers who have written a joint scientific paper. Can be described via Erdos numbers: –The Erdos number of Erdos himself is 0. – Those who co-authored a paper with Erdos have Erdos number 1. –Those who co-authored a paper with someone who have Erdos number 1 but not with Erdos have Erdos number 2, and so on.

4. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.4 The Social Web Figure 9.1 : Stanford University Social Web

5. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.5 Some Social Network Startups Friendster Dodgeball Linkedin Visiblepath Find others …

6. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.6 Social Network Terminology and Analysis Figure 9.2 : Example Collaboration Graph (Can use Pajek to analyse the network) Pajek

7. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.7 Centrality in Social Networks Figure 9.3 : Example to illustrate centrality

8. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.8 Web Communities A collection of web pages that are focused on a particular topic or theme. Can be viewed as a social network between the owners of the pages in the community. Useful definition: A set of nodes C such that for all nodes in C there are at least as many links with other nodes in C than there are with nodes outside C.

9. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.9 Peer-to-Peer (P2P) Networks A social network between computers that communicate and share information over the Internet. The computers in a P2P network can be both clients and servers. E.g.: –Gnutella –Kazaa –BitTorrent

10. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.10 Collaborative Filtering Match people with similar interests as a basis for recommendation. 1)Many people must participate to make it likely that a person with similar interests will be found. 2)There must be a simple way for people to express their interests. 3)There must be an efficient algorithm to match people with similar interests.

11. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.11 How does CF Work? Users rate items – user interests recorded. Ratings may be: – Explicit, e.g. buying or rating an item –Implicit, e.g. browsing time, no. of mouse clicks Nearest neighbour matching used to find people with similar interests Items that neighbours rate highly but that you have not rated are recommended to you User can then rate recommended items

12. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.12 Table 9.4: Example of CF MxN Matrix with M users and N items (An empty cell is an unrated item) Items / Users Data Mining Search Engines Data Bases XML Alex 1 5 4 George 2 3 4 Mark 4 5 2 Peter 4 5

13. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.13 Observations Can construct a vector for each user (where 0 implies an item is unrated) –E.g. for Alex: –E.g. for Peter On average, user vectors are sparse, since users rate (or buy) only a few items. Vector similarity or correlation can be used to find nearest neighbour. –E.g. Alex closest to Peter, then to George.

14. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.14 Case Study – Amazon.com Customers who bought this item also bought: Item-to-item collaborative filtering –Find similar items rather than similar customers. Record pairs of items bought by the same customer and their similarity. –This computation is done offline for all items. Use this information to recommend similar or popular books bought by others. –This computation is fast and done online.

15. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.15 Amazon Personal Recommendations (Figures 9.4 and 9.5)

16. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.16 Amazon Recommendations (Figure 9.6)

17. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.17 MovieLens Recommendations (Figure 9.7)

18. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.18 Challenges for CF Sparsity problem – when many of the items have not been rated by many people, it may be hard to find ‘like minded’ people. First rater problem – what happens if an item has not been rated by anyone. Privacy problems. Can combine CF with CB recommenders –Use CB approach to score some unrated items. –Then use CF for recommendations. Content-Based (CB) – use personal preferences to match and filter items –E.g. what sort of books do I like? Serendipity - recommend to me something I do not know already –Oxford dictionary: the occurrence and development of events by chance in a happy or beneficial way.

19. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.19 Weblogs (Blogs) A frequently updated site made up of entries arranged in reverse chronological order. Can be viewed as personal form of journalism. News and technology blogs are very popular: –Google blogGoogle blog –Yahoo blogYahoo blog –MSN Search blogMSN Search blog

20. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.20 Trends in Blogspace Figure 9.8 : Trend graphs for keywords “google” and “yahoo”

21. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.21 Power Law Distributions on the Web Figure 9.9 : Web inlink data from May 1999

22. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.22 What is a Power Law f(i) is the proportion of objects having property i E.g. f(i) = # pages, i = # inlinks E.g. f(i) = # sites, i = # pages E.g. f(i) = # sites i = # users E.g. f(i) = frequency of word, i = rank of word, from most freqeunt to least frequent Log-log plot - linear relationship (straight line)

23. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.23 The Evolution of the Web Figure 9.10 : Scale-free network with 130 nodes

24. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.24 Preferential Attachment Figure 9.11 : Example of preferential attachment

25. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.25 Power Laws on the Web Inlinks (2.1) Outlinks (2.72) Strongly Connected Components (2.54) No. of web pages in a site (2.2) No. of visitors to a site during a day (2.07) No. links clicked by web surfers (1.5) PageRank (2.1)

26. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.26 Small-World Networks Figure 9.14 : Emergence of small world network

27. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.27 Small-World Networks Figure 9.15 : Average distance and clustering coefficient for network in Fig. 9.14

28. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.28 Robustness and Vulnerability of a Scale-Free Network The web is extremely robust against attacks targeted at random web sites. The web is vunerable against an attack targeted at well-connected nodes. Has implications, e.g. on the spread of viruses on the Internet.

29. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.29 Searching in Social Networks – Social Navigation Figure 9.16 : Educo collaborative learning tool

30. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.30 Searching in Social Networks – Social Search Engines E.g. EureksterEurekster Figure 9.18 : Stumbleupon toolbarStumbleupon

31. Mark Levene, An Introduction to Search Engines and Web Navigation © Pearson Education Limited 2005 Slide 9.31 Navigation in Social Networks – The greedy search algorithm Figure 9.19 : A 2D lattice with random shortcuts