Trust Models for Expertise Discovery in Social Networks. September 01, Table of Contents

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1 Trust Models for Expertise Discovery in Social Networks September 01, 2003 Cai Ziegler, DBIS, University of Freiburg Table of Contents Introduction Definition and Characteristics of Trust Axes of Trust Metrics Trust Models for Expertise Discovery Trust Metrics on the Semantic Web Basic Idea of Expertise Discovery Principal Problems to Solve Framework Decomposition Trust Neighborhood Detection Global Trust Metrics with Topic Bias Rough Algorithm Sketch Closing Words Prospective Contributions Possible Roadmap 1

2 Introduction Trust and Trust Metrics Trust and the Semantic Web Rules Trust Data Proof Data Logic Selfdescribing Document Ontology Vocabulary RDF + RDFS XML + NS + XML Schema Unicode URI 2

3 Trust Merriam Webster s Collegiate Dictionary Trust is the assured reliance on the character, ability, strength, or truth of someone or something Trust between A and B with respect to T IF B can provide an answer to a question from domain T THEN Answer deemed trustworthy by A B can provide an answer to a question from domain T AND Answer deemed trustworthy by A Characteristics of Trust General complete trust Trust Agents Agents General weighted trust Trust Agents Agents [0, 1] Topic based trust Trust Agents Agents Topic [0, 1] 3

4 Trust Metrics Problem The trust relationship is not defined for any arbitrary pair of agents and generally sparse Solution Trust metrics that compute trust between arbitrary principals based upon existing trust relationships Web of Trust B C E G H Trusted Principals of A A D F Trust Metrics Operate on the Directed Trust Graph created by all personal Webs of Trust Simple trust metric IF Path (A, B) Path (A,B) < UB THEN Trust (A, B) 4

5 Trust Metrics Problem Simple trust metric not reliable Requirements Consider all relevant votes and all paths Raised impact of highly trusted agents Resolve contradictions Chain A E G Z U But E distrusts Z Bottleneck property Axes of Trust Metrics Various axes for trust metrics Global Local Distributed Centralized Applications Trust between two given principals A, B Computing all trusted nodes for one agent Group trust metrics 5

6 Trust Models for Expertise Discovery Basic Ideas of Our Approach Trust Metrics and the Semantic Web Distributed approaches fail due to poor scalability properties Any agent must keep track of all other principals Trust relationships hence publicly accessible Global metrics such as EigenTrust and PageRank not locally computable Few efforts to consider weighted trust and no efforts until now to consider topic based trust 6

7 Trust for Expertise Matchmaking Our idea Adopting the strong notion of trust Using trust to leverage knowledge exchange and service matchmaking Input : principal A, topic T Output : most relevant expert E Relevance is defined with respect to the trustworthisness of E as well as his expertise concerning T Connectivity Problems General Webs of Trust commonly highly connected Social Networks theory Six degrees of separation Low diameter B C E G H A D F Topic based Webs of Trust mostly disconnected Less arcs the more specific the topic Topics arranged in taxonomy B A C D E F G H 7

8 Connectivity Problems Hence in most cases it is impossible to establish trust paths to domain experts Our solution Asking trusted acquaintances if they know domain experts they trust Bridging the gap by relying on the general Web of Trust first: A GT C GT E GT G TT E Approach Twofold approach Calculate trusted neighborhood by relying upon centralized local group trust metrics Tailored Advogato trust metric Spreading Activation models Global trust metrics taking into account topic bias Graph Random Walk Models 8

9 Weight Assignment Problem Weighted trust does not fit easily into metrics Impact on convergence properties Interference with trust normalization B 0.7 A 0.7 C D E F G H D will be trusted more than E Question Distrust equivalent to not specifying arcs of trust? Distrust (A, B) Trust (A, B) = 0 Taxonomic Problem Topics are supposed to be arranged in one taxonomy Any agent knows this taxonomy T A B C D G H E F I J K L 9

10 Taxonomic Problem Suppose experts for L have to be discovered Agent X is trusted with respect to topic A What is the relevance of X for the query In other words In how far are any two topics related to each other What is the semantics of taxonomic arcs ST T: Trust (A, B, T) Trust (A, B, ST) Trust (A, B, ST 1 ), Trust (A,B, ST n ), T = ST 1 ST n Trust (A, B, T) Taxonomic Problem Investigation of metrics for concept similarity Number of subtopics and supertopics Number of paths between concepts Taxonomic reasoning further complicated due to weighting of trust relationships Trust (A, B, ST 1 ) = 0.7 Trust (A, B, St 2 ) = 0.3 Trust (A, B, T) =? where ST1, ST2 T How to spot relevant trust statements and merge them into one trust statement for the topic in question 10

11 Framework Decomposition Synergizing Group and Global Trust Detecting Trusted Neighborhoods Trust neighborhood detection based on general trust Personal bias indispensable for trust computation Local group trust metrics Trust information necessarily publicly accessible Similar to FOAF (Friend of a Friend) Naive approach [KR03] Based on minimum trust distance Complete lack of attack resistance Does not work for weighted trust relationships 11

12 Detecting Trusted Neighborhoods More subtle approaches Advogato group trust metric [LA03] Based upon maximum network flow in graphs Satisfies the bottleneck property of attack resistance Original algorithm for nonweighted trust only Spreading Activation models Applicability still to be evaluated Initially conceived for Latent Semantic Indexing Requires trust normalization similar to EigenTrust [KSM03] or PageRank Advogato Trust Metric Maximum network flow based on Ford-Fulkerson Node capacities determined by the capacities of previous levels divided by the average outdegree

13 Advogato Trust Metric Capacities on nodes Capacities on edges Single source / single sink Single source / multiple sinks A B A - A + B - B Super Sink Advogato Trust Metric Our research interest Tailoring Advogato to support weighted trust relationships All good properties of the metric have to be preserved Bottleneck property Restriction on the number of bad agents chosen 13

14 Spreading Activation Models Spreading Activation Models Latent Semantic Indexing Contextual Network Graphs [CCC03] Basic idea Injection of energy at some point A Energy dissipates through the network Low assigned trust value high flow resistance Own investigations Original algorithm not directly applicable Algorithm needs to be tailored and adapted Applicability not yet clear Trust Neighborhood Detection Tasks to accomplish Tailor Advogato to support weighted trust Investigate Spreading Activation Models and develop own algorithms Compare both approaches with respect to different criteria Attack resistance in different scenarios Trust normalization Impact of nodes with high outdegree 14

15 Global Metrics for Discovering Experts Local trust metrics with personal bias Restrict the range of considered agents Range may be enlarged or reduced at any time Global trust metrics Global metrics do not assume personal views of the trust graph Outperform local metrics when coping with poorly connected and disconnected graphs This is the case for topic based trust webs Global Metrics for Discovering Experts PageRank as the most popular global trust metric The higher the PageRank of pages voting for one page, the higher its own PageRank Based on finding the principal Eigenvector Adapt PageRank to support weighted trust and topic bias 15

16 Input Request (A, T) TG = (V, E), A V T TopicTax Algorithm Sketch N X; Loop NH computeneighborhood (A, N); IF max { rate (E) E calcexperts (NH, T) } < Th THEN N N + GOTO Loop ELSE RETURN E max Closing Words Prospective Contributions and Roadmap 16

17 Prospective Contributions Idea of relying upon trust networks to discover experts Topic based trust hardly investigated at the time of this writing Using trust metrics in ways they have not been used before Framework of synergizing local group trust metrics with global trust metrics biased for certain topics Tailoring existing trust metrics to support both Weighted trust Topic bias Local group trust metrics based on Spreading Activation Models Possible Roadmap Investigate local group trust metrics Advogato augmented by quantified trust Spreading Activation Models Investigate relevance for trust metrics Implement own approach Compare group trust metrics With respect to features and bias Empirical evaluations Tools for mining real trust networks Tools for visualizing computed trust neighborhoods Investigate semantics of topic based trust Search for existing approaches 17

18 The End Thanks for your attention!! 18