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Sas web 2010 lora-aroyo

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Presentation at the SASweb2010 Workshop at UMAP2010 conference

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Sas web 2010 lora-aroyo

  1. 1. Trust and Reputation in Social Internetworking Systems Lora Aroyo1 Pasquale De Meo1 Domenico Ursino2 1VU University Amsterdam, the Netherlands 2DIMET – University of Reggio Calabria, Italy
  2. 2. Social Networks Added Value !   advertise products and disseminate innovations & knowledge !   find information relevant to users !   find relevant users, e.g. LinkedIn !   spread opinions, e.g., personal, social or political !   interesting for: !   museums, broadcaster, government institutions
  3. 3. Online Identities !   Increasing number of identities !   different information sharing tasks !   connect with different communities !   UK adults have ~1.6 online profiles !   39% of those with one profile have at least two other profiles !   Companies exploring the potential of social internetworking !   Platform(s) for data portability among social networks
  4. 4. Social Internetworking System © danbri
  5. 5. What’s Needed? !   mechanisms to: !   help users find reliable users !   disclose malicious users or spammers !   stimulate the level of user participation !   deal with trust in linked data !   deal with different contexts and policies for accessing, publishing and re-distributing data
  6. 6. What’s the Goal? !   model to represent Social Internetworking components & their relationships !   understand Social Internetworking structural properties and see how it differs from traditional social networks !   model to compute trust & reputation based on linked data
  7. 7. Requirements !   trust should be tied to user’s performance, i.e., providing beneficial contributions to other users !   consider that users are involved in a range of activities, e.g., tagging, posting comments, rating !   represent a wide range of heterogeneous entities, e.g. users, resources, posts, comments, ratings and their interactions (vs. single role nodes in graphs) !   edges need to support n-ary relationships vs. binary in graphs !   multi-dimensional network vs. one-dimensional in graphs !   easy to manipulate and intuitive model
  8. 8. Graph-Based Approaches !   Model user community as graph G !   edges reflect explicit trust relationship between users !   G is sparse, thus often need for inferring trust values !   model trust & reputation in force-mass- acceleration style  capture all factors and combine them in a set of equations !   resulting model is too complicated to be handled
  9. 9. Link-Based Approaches !   link analysis algorithms, e.g. PageRank or HITS, model trust as a measure of system performance, e.g., number of corrupted files in a peer of a P2P network !   attack-resistant to manipulate reputation score !   model trust & reputation in force-mass- acceleration style  capture all factors and combine them in a set of equations !   resulting model is too complicated to be handled
  10. 10. SIS Approach !   Social Graph API (list of public URLs and connections for person p (e.g., Twitter page of p and contacts of p) !   Hypergraph !   nodes labels with object role !   multiple hyperedges between two nodes !   hyperedges – link two or more entities
  11. 11. SIS Pilot: Analysis !   We gathered from multiple social networks, e.g., LiveJournal, Twitter, Flickr: !   1, 252, 908 user accounts !   30, 837, 012 connections between users !   The probability P(k) that a user has created an account in k networks is distributed as: P(k) ~ k-4.003 !   Few users are affiliated to multiple networks !   More than 90% of users are affiliated to less than 3 networks
  12. 12. Canonization Procedures !   Map gathered data to graph with following properties: !   High network modularity, i.e., nodes tend to form dense clusters with few inter-cluster edges !   Small world phenomenon, i.e., paths between arbitrary pairs of nodes are usually short
  13. 13. Reputation in SIS !   Setting: !   users post resources &rate resources posted by others !   To compute reputation we assume that: !   User-high-reputation if he authors high quality resources !   Resource-high-quality if it gets a high average rating & posted by users with high reputation !   mutual reinforcement principle
  14. 14. Trust in SIS !   n = # of users in SIS m = # of resources they authored !   r(i) = reputation of useri q(j) = quality of resourcej !   e(j) = average rating of resourcej !   Aij = 1 if useri posted a resourcej and Aij = 0 otherwise !   r = Aq and q = AT r + e  r = (I – AAT)-1Ae . !   compute dominant eigenvector of a symmetric matrix !   easy to compute even if A gets large (AT = transpose of A and I = nxn identity matrix)
  15. 15. Future Work !   Gather a larger amount of data to analyze further the structural properties of SIS !   Test the effectiveness of the approach for reputation computing !   Test with real users in the social space of Agora (Social Event-based History browsing) and in PrestoPrime (Social Semantic Taging) . !   Ontology-based model of trust and reputation in different domains (with LOD)
  16. 16. Acknowledgements !   This research is funded by EU Marie Curie Fellowship Grant

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