1. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr Propagation Models for Trust and Distrust in Social Networks C. Ziegler, G. Lausen, Information Systems Frontiers, Vol. 7, Issue 4/5, Springer Science + Business Media, Inc., 2005 Propagation Models for Trust and Distrust in Social Networks C. Ziegler, G. Lausen, Information Systems Frontiers, Vol. 7, Issue 4/5, Springer Science + Business Media, Inc., 2005 석사 3 차 지 애 띠

2. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr Correlation between Trust & Similarity  Credibility and attack-resistance  Product-user matrix sparseness  Computational complexity & scalability

3. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr OUTLINE  INTRODUCTION  TRUST in SOCIAL NETWORKS  LOCAL GROUP TRUST METRICES ADVOGATO vs. APPLESEED  PARAMETERIZATION & EXPERIMENTS  IMPELEMENTATION & EXTENTIONS  DISTRUST  DISCUSSION

4. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr INTRODUCTION  When supposing huge network, trust judgements based on personal experience and acquaintanceship become unfeasible.  Trust was defined by Mui as the “subjective expectation an agent has about another’s future behavior based on the history of their encounters.”(Mui,2002)  Trust metrics must take into account subtle social and psychological aspects of trust and suffice criteria of computability and scalability.

5. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr TRUST in SOCIAL NETWORKS  Classification of Trust Metrics Network Perspective All peerscomplete ◦ Global scope – All peers based on complete trust graph information. Personal biaspartial ◦ Local scope – Personal bias based on partial trust graph information. Computation Locus ◦ Local approaches ◦ Centralized approaches Link Evaluation independently ◦ Scalar metrics analyze trust assertions independently, in tandem ◦ Group metrics evaluate groups of assertions “ in tandem”.

6. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr TRUST in SOCIAL NETWORKS  Classification of Trust Metrics

7. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr TRUST in SOCIAL NETWORKS  Semantic of Web Trust Semantic Web Most presented metrics and trust models have been proposed for scenario other than the Semantic Web. To define one common trust model, ◦ FOAF (“Friend of a Friend”) aims at enriching personal homepages with machine-readable content encoded in RDF statements and these pages allow their owners to nominate all individuals part of the FOAF universe. (Dumbill, 2002) ◦ Extended FOAF schema to contain trust assertions with value ranging from 1 (distrust) to 9 (trust) has been issued. (Golbeck, Parsia and Hendler, 2003) ◦ But they only capture the notation of trust and lack of trust, instead of trust and distrust. (Zero trust and distrust are not same.)

8. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr TRUST in SOCIAL NETWORKS  Semantic of Web Trust Trust Model ◦ Agent set V = {a 1, …, a n }, a ∈ V is represented and uniquely identified by the URI of their machine-readable personal homepages. ◦ Partial trust function set T = {W a1, …, W an }. Every agent a associated with one partial trust function W a : V  [0, 1] ⊥. ◦ The higher the value of W ai (a j ), the more trustworthy a i deems a j.

9. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr  Semantic of Web Trust Trust metrics for the semantic web ◦ Limitations of some distributed approaches  Trust data storage  Convergence ◦ Welcome properties of local group trust metrics  Partial trust graph exploration  Computational scalability TRUST in SOCIAL NETWORKS

10. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr LOCAL GROUP TRUST METRICES  Advogato maxflow (Levien and Aiken,2000) To discover which users are trusted by members of an online community and which or not. Arbitrary agents compute personalized lists of trusted peers, not one single global ranking. Trust computation steps ◦ Input : the number of members to trust n, the trust seed s, being a subset of entire set of users V ◦ Output : a characteristic function that maps each member to a boolean value indicating trustworthiness

11. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr  Maximum integer network flow computation (Ford and Fulkerson,1962) Capacities C V :V  N are assigned to every community members x ∈ V based upon the shortest-path distance from the seed to x. The capacity of seed is given by the input n. The capacity of each successive level is equal to that of previous level l divided by the average outdegree of edges from l. The old directed graph G = (V, E, C V ) is converted to G’ = (V’, E’, C E’ ) as single-source/single sink formulation. Advogato maxflow

12. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr  Maximum integer network flow computation (convergence) Advogato maxflow

13. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr  Algorithm 1. Trust graph conversion function transform (G = (V, E, C V ) ){ set E’  0, V’  0; for all x ∈ V do add node x+ to V’ ; add node x- to V’ ; if C V (x) >= 1 then add edge (x-, x+ ) to E’ ; set C E’ (x-, x+)  C V (x) – 1; for all (x, y) ∈ E do add edge (x-, x+) to E’ ; set C E’ (x-, x+)  ∞; end do add edge (x-, supersink) to E’ ; set C E’ (x-, supersink)  1; end if end do return G’ = (V’, E’, C E’ ) ; } Advogato maxflow

14. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr  Convergence Advogato maxflow

15. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr Advogato maxflow  Example

16. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr LOCAL GROUP TRUST METRICES  Appleseed Trust Metric Being inspired by maximum network flow computation, Appleseed is motivated by spreading activation model(Quillian,1968). Searches in contextual network graphs ◦ Edges (x, y) ∈ E ⊆ V ⅹ V (x, y ∈ V) ◦ Continuous weight W : E  [0,1] ◦ Source node s is activated through an injection of energy e ◦ e is then propagated to other nodes along edges ◦ All energy is fully divided among successor nodes wrt. their local edge weight. ◦ Supposing average outdegrees >= 1, the closer node x to the source s, the more paths leading from s to x, the higher energy flowing to x.

17. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr  Algorithm 2. Recursive energy propagation procedure energize (e ∈ R 0 +, s ∈ V ){ energy(s)  energy(s) + e ; e’  e / ∑ (s, n) ∈ E W(s, n) ; if e > T then all (s, n) ∈ E : energize (e’ ∙ W(s, n), n) ; end if } Appleseed Trust Metric

18. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr  Trust propagation Node chains ◦ Trust decay : people tend to trust individuals trusted by immediate friends more than ones trusted by friends of friends. Rank sinks : Infinite trust rank Appleseed Trust Metric

19. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr  Spreading factor in(x) : energy influx into node x. d : global spreading factor Energy d ∙ in(x) is distributed among the successors, while retaining (1-d) ∙ in(x) for itself. For instance, d = 0.85, in(x) = 5.0  energy(x) = 0.75, total energy distributed to successor 4.25 Suppose that energy(n) =0 for all n ∈ V before injection in 0 into source s. ∑ x ∈ V energy(x) = in 0 d may also be seen as the ratio between direct trust in x and trust in the ability of x to recommend others as trustworthy peers. Appleseed Trust Metric

20. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr  Rank normalization Serious interferences occur when edges are weighted. Appleseed Trust Metric

21. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr Appleseed Trust Metric  Backward trust propagation Additional “virtual” edges (x, s) from every node x ∈ V ＼ {s} to the source s, which are assigned full trust W(x, s)=1, are created.  every node is blindly trust the trust source s.  trust weight “overwritten”  Nonlinear trust normalization Situations where nodes y with poor ratings from x are awarded high trust ranks, thanks to low outdegree of x,

22. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr  Algorithm outline Input : trust seed s, trust injection e, spreading factor d ∈ [0, 1], accuracy threshold T c Output : rankings of agents wrt. the trust. Nodes are accessed only when reached by energy flow. Trust(x), energy(x) are initialized to 0. Virtual trust edges are added when x is discovered. In every iteration, the amount of incoming trust is Appleseed Trust Metric

23. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr  Algorithm outline The trust rank of x is updated as follows : trust(x)  trust(x) + (1 - d) ∙ in(x) Trust network generally contain cycles, hence the computation of in(x) is inherently recursive.  A criterion that has to be satisfied for convergence, relying on accuracy threshold T c, is needed. ◦ V i ⊆ V : set of nodes that were discovered until step i ◦ trust i (x) : current trust ranks of x for all x ∈ V ◦ ∀ x ∈ V i : trust i (x) – trust i -1(x) ≤ T c ◦ When supposing d > 0, T c > 0, trust source s part of connected component G’ ⊆ G containing at least two nodes, convergence and termination are guaranteed. Appleseed Trust Metric

24. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr Appleseed Trust Metric  Algorithm 3 Appleseed trust metric.

25. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr  Comparison of advogato and appleseed Advogato has already proven its efficiency in practical usage scenarios such as the Advogato online community. Attack resistance ◦ “bottleneck property”,“on the part of bad nodes does not affect the trust value”. Trust weight normalization ◦ Advogato encourages agents issuing numerous trust statements, while Appleseed penalizes overly abundant trust certificates.. Deterministic trust computation ◦ Linear equations allow to characterize the amount of trust assigned to x, as well as the quantity that x accords to its successor nodes. Model and output type LOCAL GROUP TRUST METRICES

26. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr PARAMETERIZATION & EXPERIMENTS  Web crawling of the Advogato community web site, extracting trust assertions stated by more than 8,000 members.  Four different levels of peer certification, “Observer”, “Apprentice”, “Journeyer”, “Master” are mapped each quantitative trust W(x, y).  Computed trust ranks may not be absolute value, just ranks. Trust injection in 0 for alignment,  Alignment heuristics - add indicator node i with W(s, i)=1 to tell whether trust injection in 0 is “good” or not.  Spreading factor - generally d = 0.85  Accuracy threshold - T c = 0.01, to make the algorithm terminate after fewer node accesses.  Convergence rate is very fast no matter how huge the number of trust nodes, large the trust injection.

27. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr PARAMETERIZATION & EXPERIMENTS  Spreading factor impact

28. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr PARAMETERIZATION & EXPERIMENTS  Convergence of appleseed

29. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr PARAMETERIZATION & EXPERIMENTS  Algorithm 4 Adding weight alignment heuristics.

30. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr IMPLEMENTATION & EXTENTIONS  Appleseed was implemented in JAVA with an architectural cushion to access “real” RDF homepages.  Testbed for local group trust metrics. Humans must be able to comprehend why agent a was accorded higher trust rank than b and come to similar results when asked for a personal judgement. yFiles library to perform all sophisticated graph drawing.

31. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr DISTRUST  Semantic of distrust Distrust as negated trust – serious implications resulting from assuming that trust multiplication and distrust to negated trust. ¬ trust (a, b) ∧ ¬ trust (b, c)  trust (a, c) Propagation of trust Distrust (a, b) ∧ trust (b, c)  distrust (a, c) Incorporating distrust into appleseed

32. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr DISTRUST  Network impact of distrust

33. INHA UNIVERSITY INCHEON, KOREA http://eslab.inha.ac.kr DISCUSSION  Appleseed can suit other application scenarios, such as group trust in online communities, open rating systems, and ad-hoc and p2p network.  Trust relationships by means of heuristics, an actual policy for eventual boolean decision-taking wrt. Which agents to grant trust and which to deny.  Local group trust metrics will become subject to substantial research for diverse computing domains within the near future.