1. Computer Science Department, University of Toronto 1 Seminar Series Social Information Systems Toronto, Spring, 2007 Manos Papagelis Department of Computer Science, University of Toronto papaggel@cs.toronto.edu

2. 2 Presentation Outline Part I: Exploiting Social Networks for Internet Search Part II: An Experimental Study of the Coloring Problem on Human Subject Networks

3. Computer Science Department, University of Toronto 3 Exploiting Social Networks for Internet Search Alan Mislove, Krishna Gummadi, and Peter Druschel, HotNets 2006 Part I

4. 4 Introduction  Social Networking (SN) A new form of publishing and locating information  Objective To understand whether these social links can be exploited by search engines to provide better results  Contributions Comparison of the mechanisms in Web and online SN for  Publishing: Mechanisms to make information available to users  Locating: Mechanisms to find information Results from an experiment in social network-based Web Search Challenges and opportunities in using Social Networks for Internet Search

5. 5 Web vs. SN (1/2) Web  Publishing: By placing documents on a Web Server (and then search for incoming links)  Locating: Via Search engines (Exploiting the link graph) Pros  Very Effective (incoming links are good indicators of importance) Limitations  No fresh data  No personalized results  Unlinked pages are not indexed

6. 6 Web vs. SN (2/2) Social Networks  Publishing: No explicit links between content (photos, videos, blogs) but implicit links between content through explicit links between users.  Locating: Navigation through the social network and browsing users’ content Keyword based search for textual or tagged content Through "Top-10" lists Pros  Helps a user find timely, relevant information by browsing adjacent regions of the network of users with similar interests  Content is rated rapidly (by comments and feedback of a community)

7. 7 Integration of Web Search and SN  Web and SN information is disjoint  No unified search tool that locates information across different systems

8. 8 PeerSpective: SN-based Web Search  Technology: Lucene text search engine and FreePastry P2P Overlay Lightweight HTTP Proxy transparently indexes all visited URLs of user

9. 9 Searching Process  A query is submitted by a user to Google  The proxy transparently forwards the query to both Google and the Proxies of Users in the network  Each proxy executes the query on the local index  Results are then collated and presented alongside Google results  Peerspective Ranking: Lucene Sc. + Pagerank + Scores from users who previously viewed the result

10. 10 Search Results Example

11. 11 Experiments  10 grad. students share downloaded or viewed Web content  One month long experiments  200.000 Distinct URLs  25% were of type text/html or application/pdf (so the can be indexed) Reports On:  Limits of hyperlink-based search  Benefits of SN-based Search

12. 12 Limits of hyperlink-based search  Report on fraction of visited URLs that are not indexed by Google Too new page (blogs) Deep Web Dark Web (no links) Results  About 1/3 of requests cannot be retrieved by Google  Peerspective’s indices covers 30% of the requested URLs  13.3% of URLs were contained in PeerSpective but not in Google's index

13. 13 Random samples of URLs not in Google and Potential Reason

14. 14 Benefits of SN-based Search  Experiments on clicks on results on first page For 1730 queries (1079 resulted in clicks) Results  86.5% of the clicked results were returned only by Google  5.7% of the clicked results were returned by both  7.7% of the clicked results were returned only by PeerSpective Conclusions  This 7.7% is considered to be the gold standard of web search engineering  Inherent advantage of using social links in web search

15. 15 Reasons for Clicks on Peerspective  Disambiguation Community tend to share definitions or interpretation of popular terms (bus)  Ranking SN information can bias the ranking algorithms to the interests of users (CoolStreaming)  Serendipity Ample opportunity of finding interesting things without searching

16. 16 Example of URLs found in Peerspective

17. 17 Opportunities and Challenges  Privacy Willingness of users to disclose information Need for mechanisms to control information flow and anonymity  Membership and Clustering of SN Users may participate in many networks Need for searching with respect to the different clusters  Content rating and ranking New approaches to ranking search results System Architecture: centralized or Distributed?

18. Computer Science Department, University of Toronto 18 An Experimental Study of the Coloring Problem on Human Subject Networks Michael Kearns, Siddharth Suri, Nick Montfort, SCIENCE, (313), Aug 2006 Part II

19. 19 Experimental Study on Human Subject Networks  Theoretical work suggests that structural properties of naturally occurring networks are important in shaping behavior and dynamics E.g. Hubs in networks are important in routing information  Empirical Structural Properties established by many disciplines Small Diameter (the “six” degrees of separation) Local clustering of connectivity Heavy-tailed distribution of connectivity (Power-law distributions)  Empirical Studies of Networks Limitation: Networks are fixed and given (no alternatives) Other approach: Controlled laboratory study

20. 20 Experiment  Experimental Scenario Distributed problem-solving from local information  Experimental Setting 38 human subjects (network vertices) Each subject controls the color of a vertex in a network Networks: simple and more complex Goal: Select a different color from that of all neighbors Problem: Coloring problem Information Available: Variable (Low, Medium, High)

21. 21 Graph Coloring Problem  Graph coloring An assignment of "colors" to certain objects in a graph such that no two adjacent objects are assigned the same color  Graph Coloring Problem Find the minimum number of colors for an arbitrary graph (NP-hard)  Chromatic number The least number of colors needed to color the graph Example  Vertex coloring  A 3-coloring suits this graph but fewer colors would result in adjacent vertices of the same color

22. 22 Network Topologies Leader Cycle Pref. Att. v=2 Pref. Att. v=3 Simple Cycle 5-Chord Cycle 20-Chord Cycle

23. 23 Information View YOU 3 6 3 7 10 YOU Overall Progress Low (Color of each Neighbor) Medium (#of Links of each Neighbor) All (All network)

24. 24 Graph Properties and Experimental Results GraphGraph PropertiesExperimental Results Colors Required Min Links Max Links Avg. Distance Avg. Exp. Duration (sec) # Exp. Solved (sec) No. of Changes Simple Cycle 2229.76144.175/6378 5-Chord Cycle 2245.63121.147/7687 20-Chord Cycle 2273.3465.676/68265 Leader Cycle 23192.3140.867/78797 Pref. Att. V=2 32132.63219.672/61744 Pref. Att. V=3 43222.08154.834/64703

25. 25 1: Collective Performance  Subjects could indeed solve the coloring problem across a wide range of networks 31/38 experiments ended in solution in less that 300 seconds 82 sec mean completion time  Collective Performance affected by network structure Preferential Attachment harder than Cycle-based networks  Cycle-based networks: Monotonic relationship between solution time and average network distance (smaller distance leading to shorter solution times)  Addition of random chords: Systematically reduces solution time

26. 26 2: Human Performance VS Artificial Distributed Heuristics Heuristic considered:  A vertex is randomly selected If there are unused colors in the neighbor of this vertex then a color is selected randomly from the available ones If there are not unused then a color is selected randomly Comparison measure  Number of vertex color changes Findings:  Results exactly reversed: lower average distance increases the difficulty for the heuristic  Preferential attachment networks easier for the heuristic

27. 27 3: Effects on Varying the Locality of Information View  Variable locality information provided to subjects Low: Their own and neighboring colors are visible Medium: Their own and neighboring colors are visible but providing information on connectivity of neighbors High: global coloring state at all times Findings:  Increased amount of information Reduces solution times for cycle-based networks Decreases solution times for preferential attachment networks Rapid convergence to one of the two solutions in cycle-based networks

28. 28 Information View Effect 1: Pref. Att. VS Cycle-based Networks

29. 29 Information View Effect 2: Cycle-based Solution Convergence Low Information View High Information View Population oscillates between approaches to the two solutions Rapid convergence to one of the Two possible solutions

30. 30 Individual Strategies  Choosing colors that result in the fewest local conflicts  Attempt to avoid conflicts with highly connected subjects  Signaling behavior of subjects  Introducing conflicts to avoid local minima

31. Computer Science Department, University of Toronto 31 Questions?

32. Computer Science Department, University of Toronto 32 Thanks!