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

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

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

Social Internetworking System

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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

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

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

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

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

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

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

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

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

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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)

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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)

!   This research is funded by EU Marie Curie Fellowship Grant

Acknowledgements