Decentralized Search

Transcription

1 Link Analysis and Decentralized Search Markus Strohmaier, Denis Helic Multimediale l Informationssysteme t II 1

2 The Memex (1945) The Memex [Bush 1945]: B A mechanized private library for individual use Mimics i associative memory where users can insert documents navigate documents retrieve documents build trails through documents A (i) (ii) p C (iii) C s Cs interaction with documents is mediated by user A and B [Bush 1945] V. Bush. As We May Think. Atlantic Monthly, Operated and maintained individually But trails can be shared socially e.g. (i) a user A can send trail to user B (ii) user B modifies and shares it with user C (iii) user C uses the trail for navigation 2

3 Web based Retrieval: Challenges Working with an enormous amount of data 10 billion pages a 500kB estimated in pages / person on the globe 20 times larger than the LoC print collection estimated in 2003 Furthermore there is a Deep Web 550 billion pages estimated in

4 Web based Retrieval: Challenges Example for the amount of web pages: Searching for Star Trek yielded about 11 million of results on Google [Nov 2007] Ordinary users investigate result list entries. What web page is the most interesting? How to store an index (inverted file) with this size? 4

5 Web based Retrieval: Challenges The Web is highly hl dynamic Study by Cho & Garcia-Molina (2002): 40% of the web pages changed their dataset t within a week 23% of the.com pages changed on daily basis Study by Fetterly et al. (2003): 35 % of the pages changed during investigations Larger web pages change more often 5

6 Web based Retrieval: Challenges The Web is self-organized No central authority (for the WWW) or main index Everyone can add (even edit) pages Pages disappear on regular basis A US study claimed that in 2 investigated tech. journals 50% of the cited links were inaccessible after four years. Lots of errors and falsehood, no quality control 6

7 Web based Retrieval: Challenges The Web is hyperlinked Based on HTML Markup tags and URIs Pages are interconnected Unidirectional links (in-link, out-link, self-link) Network structures emerge from the links Link analysis is possible 7

8 Common Architecture 8

9 The World Wide Web ( ) A user s interaction with the web is mediated by (a few) editors and publishers 9

10 The World Wide Web Today (2010) Interaction between individuals and computational systems is mediated by the aggregate behavior of massive numbers (millions) of users. 10

11 Social Computation influences system properties (X) X=Findability X=Utility Emergent system properties are beyond the direct control of engineers. New methods and algorithms for designing i and shaping socialcomputational systems are needed. It is through the process of social computation, i.e. the combination of social behavior and algorithmic computation, that desired and undesired system properties p and functions emerge. X=Navigability X=Relevance 11

12 Example: X = Connectivity (of the web graph) Questions: What is X like? What causes X? bow-tie architecture of the web [Broder et al 2000] 12

13 Example: X = Connectivity (of the web graph) Questions: What is X like? bow-tie architecture of the web What causes X? How can we Social mechanisms, such as improve X? preferential attachment Preferential attachment: Degree of vertex i an open problem e The sum of all vertices degrees [Broder et al. 2000] [Barabasi and Albert 1999] Probability of a new vertex attaching to a vertex i with degree k [Barabasi and Albert 1999] A.-L. Barabási, R. Albert, Emergence of Scaling in Random Networks, Vol no. 5439, pp , Science, 15 October [Broder et al. 2000] A. Broder, R. Kumar, F. Maghoul, P.Raghavan, S. Rajagopalan, R. Stata, A. Tomkins, and J. Wiener. Graph structure on the web. In 9th International WWW Conference,

14 Analysis of Dynamic Links in Social Tagging Systems How can navigability in social tagging systems be described d and improved? D. Helic, C. Trattner, M. Strohmaier and K. Andrews, On the Navigability of Social Tagging Systems, The 2nd IEEE International Conference on Social Computing (SocialCom 2010), Minneapolis, Minnesota, USA, (acceptance rate 33/245, 13,47% quota, nominated for Best Paper). 14

15 Structure of Social Tagging Systems: Definition Resources Tags User A folksonomy is a tuple F:= (U, T, R, Y) where the three disjoint, i finite it sets U, T, R correspond to user 1 a set of persons or users u U a set of tags t T and a set of resources or objects r R Y U T R, called set of tag assignments tag 1 res. 1 navigation [Hotho et al 2006] 15

16 Tag Clouds are Assumed to be Efficient Tools for Navigation The Navigability Assumption: An implicit assumption among designers of social tagging systems that tag clouds are specifically useful to support navigation. This has hardly been tested or critically reflected in the past. web Navigating tagging systems via tag clouds: 1. The system presents a tag cloud to the user. 2. The user selects a tag from the tag cloud. 3. The system presents a list of resources tagged with the selected tag. 4. The user selects a resource from the list of resources. 5. The system transfers the user to the selected resource, and the process potentially starts anew. Navigating Y T R 16

17 Navigability Informal Description: If / how quick one can get from document A to document Bi in a hypertext t system (more precise definition follows later) Designing for Navigability: In traditional hypertext systems, this property p used to be within the control of system designers 17

18 Defining Navigability A network is navigable iff: There is a path between all or almost all pairs of nodes in the network. [Kleinberg 1999] Formally: 1. There exists a giant component: size(gc) > 0.9 * n a single connected component that accounts for a significant fraction of all nodes 2. The effective diameter d eff is low: d eff < log n d eff = distance at which 90% of pairs of nodes are reachable n number of nodes in the network [Kleinberg 1999] J. Kleinberg. The small-world phenomenon: An algorithmic perspective. Proc. 32nd ACM Symposium on Theory of Computing,

19 Example 1: Navigability: Examples Not navigable: No giant component Example 2: Not navigable: giant component, BUT avg. shortest path > log 2 (9) 19

20 Example 3: Navigability: Examples Navigable: Giant component AND avg. shortest path 2 < log(9) 2 Is this efficiently navigable? There are short paths between all nodes, but can an agent or algorithm find them with local knowledge only? 20

21 Efficiently navigable A network is efficiently navigable iff: If there is an algorithm that can find a short path with only locall knowledge, and the delivery time of the algorithm is bounded polynomially by log k (n). Example 4: B A C Efficiently navigable, if the algorithm knows it needs to go through A B C J. Kleinberg. The small-world phenomenon: An algorithmic perspective. Proc. 32nd ACM Symposium on Theory of Computing, Also appears as Cornell Computer Science 21 Technical Report (October 1999)

22 Navigability of Social Tagging Systems But: how doesdatasets Annotations Resources (i) the size of tag Austria clouds Forum and 32,245 12,837 (ii) number of resources Bibsonomy / tag 916, ,339 influence the navigability (X 1 ) of social tagging systems? CiteULike 6,328,021 1,697,365 established systems, many users New system, few users Navigable in theory: GC exists, low eff. diameter Shrinking diameter over time, cf. [Leskovec et al. 2005] (for Y T R) [Leskovec et al. 2005] J. Leskovec, J.M. Kleinberg, C. Faloutsos: Graphs over time: densification laws, shrinking diameters and possible explanations. KDD 2005:

23 Modeling UI constraints Tag Cloud Size n number of n tags displayed per resource (with a topn algorithm) Pagination of resources / tag number of k resources displayed per tag (with reverse chronological ordering) 23

24 How UI constraints effect Navigability Tag Cloud Size Pagination Limiting the tag cloud size n to practically feasible sizes (e.g. 5, 10, or more) does not influence navigability (this is not very surprising). BUT: Limiting the out-degree of high frequency tags k (e.g. through pagination with resources sorted in reverse-chronological order) leaves the network vulnerable to fragmentation. This destroys navigability of prevalent approaches to tag clouds. 24

25 Findings 1. For certain specific, but popular, tag cloud scenarios, the so-called Navigability Assumption does not hold. 2. While we could confirm that tag-resource networks have efficient navigational properties in theory, we found that popular user interface decisions significantly impair navigability. These results make a theoretical and an empirical argument against existing approaches to tag cloud construction. How can we recover navigability of social tagging systems? 25

26 Recovering Navigability in Social Tagging Systems Instead of reverse-chronological ordering of resources, we apply a naive random ordering. Based on this observation, we have developed ordering algorithms that balance semantic and navigational aspects, eg e.g. [Trattner et al. 2010] [Trattner et al. 2010] C. Trattner, M. Strohmaier, and D. Helic. Improving navigability of hierarchically-structured encyclopedias through effective tag cloud construction. In 10th International Conference on Knowledge Management and Knowledge Technologies I-KNOW 2010, Graz, Austria,

27 Navigating g Networks How can model user navigation on networks? 27

28 Experiment [Milgram] Goal Define a single target person and a group of starting persons Generate an acquaintance chain from each starter to the target Experimental Set Up Each starter receives a document was asked to begin moving it by mail toward the target Information about the target: name, address, occupation, company, college, year of graduation, wife s name and hometown Information about relationship (friend/acquaintance) [Granovetter 1973] Constraints starter group was only allowed to send the document to people they know and was urged to choose the next recipient in a way as to advance the progress of the document toward the target 28

29 Introduction The simplest way of formulating the small-world problem is: Starting with any two people in the world, what is the likelihood that they will know each other? A somewhat more sophisticated formulation, however, takes account of the fact that while person X and Z may not know each other directly, they may share a mutual acquaintance - that is, a person who knows both of them. One can then think of an acquaintance chain with X knowing Y and Y knowing Z. Moreover, one can imagine circumstances in which X is linked to Z not by a single link, but by a series of links, X-A-B-C-D Y- Z. That is to say, person X knows person A who in turn knows person B, who knows C who knows Y, who knows Z. [Milgram 1967, according to ] 29

30 An Experimental Study of the Small World Problem [Travers and Milgram 1969] A Social Network Experiment tailored towards Demonstrating Defining And measuring Inter-connectedness in a large society (USA) A test of the modern idea of six degrees of separation Which states that: every person on earth is connected to any other person through a chain of acquaintances not longer than 6 30

31 Results I How many of the starters would be able to establish contact with the target? 64 out of 296 reached the target How many intermediaries would be required to link starters with the target? Well, that depends: the overall mean 5.2 links Through hometown: 6.1 links Through hbusiness: 46li 4.6 links Boston group faster than Nebraska groups Nebraska stockholders not faster than Nebraska random What form would the distribution of chain lengths take? 31

32 Results III. Common paths Also see: Gladwell s Law of the few 32

33 Follow up work (2008) Horvitz and Leskovec study billion conversations among 240 million people of Microsoft Messenger Communication graph with 180 million nodes and 1.3 billion undirected edges Largest social network constructed and analyzed to date (2008) 33

34 Decentralized Search Then, the performance of decentralized search Background knowledge: depends on the suitability of folksonomies. (a tag hierarchy) Idea: use folksonomies as background knowledge Shortest path to target In other words, we can evaluate the suitability of folksonomies for decentralized search through simulations. Folksonomy 1 Folksonomy... Folksonomy n A (tag-tag) network: shortest path found with locall knowledge p LK = 4 Goal: Navigate from START to TARGET Δ = p LK -p GK using local and background knowledge only candidates start target shortest path with global knowledge p GK = 3 J. Kleinberg. The small-world phenomenon: An algorithmic perspective. Proc. 32nd ACM Symposium on Theory of Computing, Also appears as Cornell Computer Science Technical Report (October 1999) 34

35 Evaluating Hierarchical Structures in Networks How can measure the efficiency of hierarchical structures t for navigation? 35

36 The World Wide Web ( ) How efficient is this as a navigational aid? 36

37 Construction of hierarchies from unstructured tagging data From tag centrality to high tag tag generality: centrality: more abstract low tag centrality: more specific Other existing folksonomy algorithms: k-means, affinity propagation, [Heyman and Garcia-Molina 2006] 37

38 Evaluation Framework Decentralized Search Folksonomy 1 Simulation Performance Evaluation which folksonomy performs best on a given navigational task Folksonomy Click-Data Explanatory Evaluation which folksonomy explains actual user behavior best Folksonomy n 38

39 Success Rates Across Different Folksonomies flickr dataset Tag generality approaches k-means / affinity propagation Success rate: The number of times an agent is successful in finding a path using a particular folksonomy as background knowledge max hops n: the maximal number of steps an agent is allowed to perform before stopping (a tunable parameter e.g., an agent only follows n links). n Random folksonomy All approaches outperform a random folksonomy Tag generality approaches outperform k-means / Aff. Propagation 39

40 Success Rates Across Different Datasets Holds for all datasets (to diff. extents) Efficiency: how often does an agent not find the global shortest path, but some other path that is longer. But how efficient are those folksonomies during search? 40

41 Stretch Δ =p LK -p GK Shortest Paths found with Local Knowledge Bibsonomy K-Means Finds no path: Δ = infinite Finds paths that is +1 longer: Δ = 1 Holds for all datasets t Finds shortest possible path: (to diff. Δ = 0 extents) Tag generality approaches (d+e) find much shorter paths! 41

42 Conclusions Dsearch as a natural model of user navigation on the web Emergence of dynamic, user-generated links reduces control Empirical studies and new algorithms are needed to recover important system properties 42

43 End of Presentation Acknowledgements 43