A Novel Trust Computing System for Social Networks - IEEE Xplore

2011 IEEE International Conference on Privacy, Security, Risk, and Trust, and IEEE International Conference on Social Computing

A Novel Trust Computing System for Social Networks Justin Zhan and Xing Fang Department of Computer Science North Carolina A&T State University Greensboro, NC, 27410 zzhan,[email protected] is psychologically depending on a variety of factors, such as first impressions, physical proximity, and compatibility, etc. Of all the factors, trust plays the predominant role during a relationship construction, continuation, and deterioration [2]. By definition, trust can be interpreted as “the subjective probability by which an individual, A, expects that another individual, B, performs a given action on which its welfare depends [3].” Other than social networks, many other online networks also involve trust. For examples, online shopping networks, like Amazon and eBay, allow buyers to evaluate sellers by posting comments and trust ratings after every purchase. Online P2P lending network, prosper.com [4], is based on mutual trust [5]. In social networks, trust has been intensively studied on propagations. Trust Inference Mechanisms are introduced to calculate an accurate trust value between two individuals who may not have a direct connection on a social network. Liu et al. [6] categorized existing Trust Inference Mechanisms into three different types including multiplicative, average, and probabilistic mechanisms. All of these mechanisms essentially rely on trust ratings for the actual trust computations. The implementation of trust ratings may appropriate for the sake of trust inference computing. It is not thorough enough to compute trust magnitude for two directly connected individuals, by only applying such ratings. In this paper, we claim that trust in between two immediate individuals on social networks can be evaluated via three different perspectives: profile similarity, information reliability, and social opinions. We propose a trust computing system for trust computations in social networks. The system consists of three trust computing components, each of which is to compute and return a trust value from the aforementioned three perspectives. On the basis of the trust values, a trust score indicating the trust magnitude is eventually computed by the whole system. Our contributions are: • To the best of our knowledge, this is the first effort that a trust computing system is proposed for general social networks. • The trust computing system examines profile similarity as the first trust computing method.

Abstract—Trust is a phenomenon that is exclusively possessed by human beings. Due to its human-related properties, trust is difficult to be uniformly defined or even be precisely described. As a research field, trust has been intensively focused on exploring propagations and usefulness in social networks. Little research work has been found on simulating trust itself. In this paper, we present a trust computing system to tackle the problem of trust simulation. The system is capable of simulating trust between two directly connected individuals on social networks. By integrating the trust values computed through three trust computing components, the system eventually returns a trust score representing the actual trust magnitude from one particular individual to the other. To our best knowledge, this is the first effort that such a trust computing system is proposed. Keywords: trust; social network; trust computing system; trust score I.

INTRODUCTION

Nowadays, social network has been a popular term that holds different meanings. A traditional definition for social network given by Walker et al. [1] describes a social network as “a set of personal contracts through which the individual maintains his social identity and receives emotional support, material aid and services, information and new social contacts.” As a contemporary concept, social network also refers to several cyber applications including online social websites, blogs, and forums. Despite the difference between traditional and contemporary views, a social network resembles a network structure, where nodes and ties indicate people and their connections, respectively. The connections, in turn, represent different relationships existing among the people, such as business contractors, colleagues, friends, and relatives. Some relationships, like kinships, may be considered stable, while others can be easily affected. Consequently, the size of a social network changes from time to time. For instance, it can be increased by building up more friendships or can be reduced by terminating some business contracts. According to Levinger [2], an interpersonal relationship 978-0-7695-4578-3/11 $26.00 © 2011 IEEE DOI

1284

Our system adopts Information Theory for the information reliability computation, which provides a practical solution in evaluating personal reliabilities from the point view of information communication. • The system incorporates trust ratings as a complementary method in evaluating trust between individuals. The remainder of the paper is organized as follows: We present our preliminaries in Section 2. In Section 3, the detailed system description is provided. We provide further discussion in Section 4. We give our cloclusion in Section 5. II.

PRELIMINARIES

The trust computing system is a novel trust computation system that is designed to calculate trust magnitude between two directly communicating individuals on social networks. The system consists of three trust computing components, which are designed for quantifying profile similarity, information reliability, and social opinions, respectively. The rest of this section explores their relationships with trust as well as presents the definitions of the terms that are used throughout this paper. A. Profile Similarity

Early social psychological research shows that people are willing to communicate with the ones who are similar to themselves. The term, Homophily, was first introduced by Lazarsfeld and Merton [7] in their study of friendship process. Later, McPherson et al. [8] addressed that “homophily is the principle that a contact between similar people occurs at a higher rate than among dissimilar people.” They [8] also summarized the characteristics determining homophilies, like race, ethnicity, sex, age, religion, education, occupation, and behavior patterns. Based on the social psychological evidence, Ziegler and Golbeck [9] explored the correlation between trust and profile similarity by introducing their trust framework. The framework provides the profile similarity computation, which can be considered as a practical way for trust measurement when there is no other trust evidence available. Profile similarity is also particularly useful for predicting trend of trust [10]. Hence, we apply profile similarity computation as the first trust computing component of our trust system. Definition 1. A profile of an individual A is a collection of A’s characteristics that determine the homophily with any other individuals on A’s social network.

A’s profile can be formally denoted as: ”‘ϐ‹Ž‡୅ ൌ ሼŠƒ”୧ ȁ‹ ൌ ͳǡ ǥ ǡ ሽǡ  ‫ א‬Գ. A characteristic of ”‘ϐ‹Ž‡୅ is a collection of concepts denoted as: Šƒ” ൌ ൛‘…‡’– ୨หŒ ൌ ͳǡ ǥ ǡ ൟǡ  ‫ א‬Գ . Thus, ”‘ϐ‹Ž‡୅ can be also formally denoted as: ”‘ϐ‹Ž‡୅ ൌ ሼ‘…‡’– ୩ ȁ ൌ ͳǡ ǥ ǡ  ‫ כ‬ሽ. Definition 2. Profile similarity defines the extent of both profiles’ semantic similarity. B. Information Reliability

Communication is the crucial process in forming up a social network. Most relationships cannot be built without communications. Communication is the activity of conveying information. In terms of trust, information can be defined as a totality of reliable, neutral, and unreliable information [11]. Reliable information generates social consequences that are consistent with truth while unreliable information leads to the consequences that are not consistent with truth. And, neutral information does not invoke any social consequences. Hence, we adopt our previous work of information reliability as the Reliability of the trust computing system. Definition 3. ˆ‘”ƒ–‹‘‡Ž‹ƒ„‹Ž‹–› ൌ

ୖୣ୪୧ୟୠ୪ୣ୍୬୤୭୰୫ୟ୲୧୭୬ . ୍୬୤୭୰୫ୟ୲୧୭୬

C. Social Opinions

An opinion is a view or judgment formed in the mind about a particular matter. In real life, people frequently seek opinions before they made their own decisions. For example, before deciding whether to visit a restaurant you have never been, you may want to have some opinions about the restaurant from other people. Logically, decent opinions prompt the visiting while unpleasant ones hinder the going. In trust domain, opinions are taking into account in evaluating someone’s trustworthiness. Online reputation systems [12] gather opinions about a particular seller’s trustworthiness from buyers, for the computation of the seller’s reputation. Motivated by the reputation system, our third trust computing component takes such opinions into account. We then claim that one individual’s trustworthiness can be somewhat identified through other people’s social opinions (trust ratings). TRUST COMPUTING SYSTEM In this section, we present each component of the trust computing system. III.

1285

1: for every ‹ˆ”‘ͳ–‘ do 2: for every Œˆ”‘ͳ–‘ do 3: Šƒ”‡…–‘”୧୅ ՚ ‹•–ƒ…‡ሺ‘…‡’– ୅ ୨ ǡ ୨ ሻ 4: Šƒ”‡…–‘”୧୆ ՚ ‹•–ƒ…‡ሺ‘…‡’– ୆୨ ǡ ୨ ሻ 5: return Šƒ”‡…–‘”୧୅ and Šƒ”‡…–‘”୧୆

A. The Trust Computing Component 1

By Definition 2, profile similarity is measured via sematic similarity, which can be obtained by exploring the similarities between concepts that belong to the same characteristics in two profiles. In practice, the computation of semantic similarity is deployed based upon ontologies. An ontology is a specification of a conceptualization that describes concepts and relationships used within a community [13]. There are a few published ontologies such as Gene Ontology and WordNet. An example of an ontology structure is shown in Figure 1.

The function ‹•–ƒ…‡ሺšǡ ›ሻ returns the shortest distance from concept › to š , based on ontology structure. Once the characteristic-vectors of A and B are acquired, we apply cosine similarity to calculate both individuals’ CharVector Similarity: Šƒ”‡…–‘”୧ ‹ሺǡ ሻ Šƒ”‡…–‘”୧୅ ‫”‘–…‡”ƒŠ כ‬୧୆ ൌ ฮŠƒ”‡…–‘”୧୅ ฮ ‫ כ‬ฮŠƒ”‡…–‘”୧୆ ฮ Consequently, the profile similarity of both individuals is calculated as: σ୬୧ୀଵ Šƒ”‡…–‘”୧ ‹ሺǡ ሻ ”‘ϐ‹Ž‡‹ሺǡ ሻ ൌ  2)

Figure 1. A Ontology Structure Example The ontology example in Figure 1 resembles as a tree. The concepts, “Action Movie” and “Comic Movie,” are two successors of the concept “Movie.” In other words, “Movie” is a common ancestor of “Action Movie” and “Comic Movie” within this ontology structure. Similarly, “Entertainment” is an ancestor of “Movie,” while “Movie” is a successor of “Entertainment.” On the basis of ontology structure, we present our similarity computation methods in the rest of this subsection. 1)

Concept-based Profile Similarity

By definition 1, A and Bs’ profiles can also be denoted based upon concepts, which ”‘ϐ‹Ž‡୅ ൌ ሼ‘…‡’– ୩ ȁ ൌ ͳǡ ǥ ǡ  ‫ כ‬ሽ and ”‘ϐ‹Ž‡୆ ൌ ൛‘…‡’– ᇱ ୩ห ൌ ͳǡ ǥ ǡ  ‫ כ‬ൟ. Concept is the smallest profile element regarding the definition. It is also the element that directly related to the ontology structure. In fact, the ontology structure can be much more complex than the sample tree structure. For every concept, it may have multiple ancestors making the ontology structure a directed graph. In this case, we define the frequency of a concept … under Šƒ”୧, as: ”‡“ሺ…ሻ ൌ —„‡”‘ˆ……—”ƒ…‡‘ˆ… ൅ —„‡”‘ˆ……—”ƒ…‡‘ˆ… ᇱ ••—……‡••‘”Ǥ The probability of … under Šƒ”୧ is: ୊୰ୣ୯ሺୡሻ . ”‘„ሺ…ሻ ൌ σ

CharVector-based Profile Similarity

Recall the definition 1, individual A’s profile is denoted as ”‘ϐ‹Ž‡୅ ൌ ሼŠƒ”୧ ȁ‹ ൌ ͳǡ ǥ ǡ ሽǡ  ‫א‬. Correspondingly, we have individual B’s profile denoted as ”‘ϐ‹Ž‡୆ ൌ ሼŠƒ”୧ȁ‹ ൌ ͳǡ ǥ ǡ ሽǡ  ‫ א‬Գ. For each Šƒ”୧ ‫ א‬ሺ‘”ሻ, we have ୅ሺ୆ሻ ୅ሺ୆ሻ ൌ ቄ‘…‡’– ୨ ቚŒ ൌ ͳǡ ǥ ǡ ቅ ǡ  ‫ א‬Գ. Šƒ”୧

ౙ‫ిא‬౞౗౨౟ ୊୰ୣ୯ሺୡሻ

The information content of a concept … is: ሺ…ሻ ൌ െŽ‘‰ሺ”‘„ሺ…ሻሻ. The common ancestors of two concepts are defined as: ‘‘…ሺ…ǡ … ᇱ ሻ ൌ …‡•–‘”•ሺ…ሻ ‫•”‘–•‡… ת‬ሺ… ᇱ ሻǤ According to Resnik [14], the semantic similarity between two concepts is their shared information, which in turn can be defined as the information content of both concepts’ most informative common ancestor: ‹ୖୣୱ୬୧୩ ሺ…ǡ … ᇱ ሻ ൌ Šƒ”‡ሺ…ǡ … ᇱ ሻ ൌ ƒšሼ ሺƒሻȁƒ ‫…‘‘ א‬ሺ…ǡ … ᇱ ሻሽǤ ᇱ Given ܿ and ܿ ǡ Jiang and Conrath [15] defined their semantic distance as: †‹•–ሺ…ǡ … ᇱ ሻ ൌ ሺ…ሻ ൅ ሺ… ᇱ ሻ െ ʹ ‫‡”ƒŠ כ‬ሺ…ǡ … ᇱ ሻ.

Given the ontology structure, for each pair of ‘…‡’– ୅ ୨ and ‘…‡’– ୆୨ , there is at least a nearest common ancestor, denoted as ୨ . The algorithm for the characteristic-vectors (CharVector) computing for both individuals A and B is: The CharVector Algorithm Input: ”‘ϐ‹Ž‡୅ and ”‘ϐ‹Ž‡୆ Output: Šƒ”‡…–‘”୧୅ and Šƒ”‡…–‘”୧୆

1286

certain entities or events. More often than not, we do receive opinions about one of our friends from the others. For instance, person A may receive opinions about person B from some of their mutual friends. Based on this truth, we claim that person A’s trust magnitude towards person B can be effected by the opinions about person B, in terms of trust, from A and Bs’ mutual friends. For the sake of computational modeling, we adopt Dirichlet Reputation System [17]. The reasons are: 1) Reputation system has been widely applied especially for online shopping scenarios; 2) Reputation system is able to return a reputation value of a particular online seller in real time; 3) Dirichlet Reputation System enables multidimensional ratings instead of the binary ratings. This is important because trust magnitude itself is a value falling into ሾͲǡͳሿ. The Dirichlet probability density function is: ୻ሺ஑బ ሻ ς୩୧ୀଵ ’ሺɅ୧ ሻ஑ሺ஘౟ሻିଵ, ˆሺ’ȁȽሻ ൌ ౡ

They defined the semantic similarity, based on the ଵ semantic distance, as: ‹୎େ ሺ…ǡ … ᇱ ሻ ൌ ୢ୧ୱ୲ሺୡǡୡᇲ ሻାଵ. And Lin [16] addressed the semantic similarity is: ‹୐୧୬ ሺ…ǡ … ᇱ ሻ ൌ

ଶ‫כ‬ୗ୦ୟ୰ୣሺୡǡୡᇲ ሻ Ǥ ୍େሺୡሻା୍େሺୡᇲ ሻ

Based on the study of these proposed semantic similarity computation methods, we propose our profile similarity as: ”‘ϐŽ‹‡‹ሺǡ ሻ σୡ‫א‬େ୭୬ୡୣ୮୲ ‫׫‬ୡᇲ ‫א‬େ୭୬ୡୣ୮୲ ᇲ ‹ሺ…ǡ … ᇱ ሻ ౡ ౡ ൌ ǡ ‹ሺ…ǡ … ᇱ ሻ ‫כ‬ ‫ א‬ሼ‹ୖୣୱ୬୧୩ ǡ ‹୎େ ǡ ‹୐୧୬ ሽ B. The Trust Computing Component 2

In our previous research [11], we explored the relationship between information reliability and trust, via the Entropy ሺšሻ shown in Figure 2.

ς౟సభ ୻൫஑ሺ஘౟ ሻ൯

™Š‡”‡Ʌ୧ ൌ ሼɅଵ ǡ ǥ ǡ Ʌ୩ ሽǡ Ƚ଴ ൌ σ୩୧ୀଵ ȽሺɅ୧ ሻ ǡ σ୩୧ୀଵ ’ሺɅ୧ ሻ ൌ ͳ . For the reputation system, Ʌ୧ denotes the actual reputation rating at level i; ȽሺɅ୧ ሻ denotes the number of rating at level i; Ƚ଴ denotes total number of ratings; ’ሺɅ୧ ሻ denotes the probability of the reputation rating at level i. As an example, we assume there exists a reputation system with five levels of reputation rating: Ʌଵ ൌ ‡”ˆ‡…–ǡ Ʌଶ ൌ ‘‘†ǡ Ʌଷ ൌ ‡†‹‘…”‡ǡ Ʌସ ൌ ƒ†ǡ Ʌହ ൌ ‘”•– and ȽሺɅଵ ሻ ൌ ͳͲǡ ȽሺɅଶ ሻ ൌ ͺǡ ȽሺɅଷ ሻ ൌ ͸ǡ ȽሺɅସ ሻ ൌ ʹǡ ȽሺɅହ ሻ ൌ Ͳ . Therefore, Ƚ଴ ൌ σହ୧ୀଵ ȽሺɅ୧ ሻ ൌ ͳͲ ൅ ͺ ൅ ஑ሺ஘ ሻ ͸ ൅ ʹ ൅ Ͳ ൌ ʹ͸ and ‫ א ‹׊‬ሾͳǡͷሿǣ൫’ሺɅ୧ ሻ൯ ൌ  ஑ ౟ .

Figure 2. P and H(P) We defined: ˆ‘”ƒ–‹‘‡Ž‹ƒ„‹Ž‹–› ൌ

‡Ž‹ƒ„Ž‡ ˆ‘”ƒ–‹‘ ‫ ؝‬Ǥ ˆ‘”ƒ–‹‘

The Entropy ሺšሻ is: ሺšሻ ൌ െ Ž‘‰ሺሻ െ ሺͳ െ ሻ Ž‘‰ሺͳ െ ሻ ‫ ؝‬ሺሻǤ Assuming that neutral information is ignored, we only consider the binary case, in which there only exists reliable information and unreliable information. Thereby, information reliability is: ”‡Ž‹ƒ„Ž‡™‹–Š–Š‡’”‘„ƒ„‹Ž‹–› . ൜ —”‡Ž‹ƒ„Ž‡™‹–Š–Š‡’”‘„ƒ„‹Ž‹–›ሺͳ െ ሻ ሺšሻ can also be treated as a totality of information content of reliable information and unreliable information, where ሺšሻ ൌ  ‫ כ‬ሺሻ ൅ ሺͳ െ ሻ ‫ כ‬ሺͳ െ ሻ. Given that trust magnitude is falling into the closed interval, ሾͲǡͳሿ, where 0 stands for no trust and 1 means full trust, we have the following trust computing method: ሺͲǤͷሻ െ ሺሻǡ  ‫ א‬ሺͲǤͷǡͳሿ ሺሻ ൌ ൜ Ͳǡ  ‫ א‬ሾͲǡͲǤͷሿ ሺሻ is the trust magnitude and ሺሻ ‫ א‬ሾͲǡͳሿ.

బ

Similarly, we use Ʌ୧ to represent the actual trust rating at level i, where Ʌ୧ can be matched to any trust rating belonging to ሾͲǡͳሿ. We calculate the trust magnitude, ”, based on trust ratings (opinions) as: ” ൌ σ୩୧ୀଵ Ʌ୧ ‫ כ‬൫’ሺɅ୧ ሻ൯ǡ ™Š‡”‡” ‫ א‬ሾͲǡͳሿ. D. The Trust Computing System

The trust computing system aims to return a final trust score for two connected individuals on social networks. It is well known that trust is asymmetric [6]. In other words, person A fully trust person B does not necessarily mean that person B fully trust person A. In this case, the comprehensive trust score only indicates trust magnitude from one of the two individuals’ perspective. We propose that the trust computing system, under A’s perspective, in calculating B’s trust magnitude is denoted as: ”—•–…‘”‡ሺȁሻ ൌ ρଵ ‫‘” כ‬ϐ‹Ž‡‹ሺǡ ሻ ൅ ρଶ ‫ כ‬ሺ୆ ሻ ൅ ρଷ ‫” כ‬୆ ǡ

C. The Trust Computing Component 3

It is widely believed that people are willing to seek opinions, in order to adjust their own judgments towards

1287

whereρଵ ǡ ρଶ ǡ ƒ†ρଷ are three balancing factors that Ͳ ൑ ρଵ ǡ ρଶ ǡ ρଷ ൑ ͳ, ρଵ ൅ ρଶ ൅ ρଷ ൑ ͳand ”‘ϐ‹Ž‡‹ሺǡ ሻ ൌ ”‘ϐ‹Ž‡‹ሺǡ ሻ ‫ א‬ሾͲǡͳሿ. ୆ is B’s information reliability according to A. The magnitude of ୆ essentially depends on how much reliable information B sends to A. ”୆ is B’s trust magnitude deduced from the trust ratings of B gathered by A. Both ሺ୆ ሻ and ”୆ fall into ሾͲǡͳሿ. Therefore, the comprehensive trust score of B according to A is ‘”—•–…‘”‡ሺȁሻ ‫ א‬ሾͲǡͳሿ. IV.

EXPERIMENT

In this section, we conduct experimental evaluations towards our proposed trust computing system. All of the trust components are evaluated.

Figure 4 The Information Reliability The trust ratings are shown in Figure 5:

A. Data Collections and Experimental Environment

To compute the profile similarity, 40 friends’ profiles were collected via online social networks. Each person’s profile has seven categories: Basic Information, Featured People, Education and Work, Philosophy, Arts and Entertainment, Interests, and Contact. In our experiment, we treat all of the seven categories as the characteristics referred in Definition 1. For the computation of information reliability, we use 1600 emails to simulate the communication between each friend, in which 1000 emails are legitimate and 600 are spams. The legitimate emails are treated as reliable information while the spams are treated as unreliable information. Additionally, 40 online sellers reputation information was collected for the trust computing component 3. The evaluations were deployed on a system running a Windows 7 64-bit OS, with Intel i-7 CPU, 4GB RAM and 500GB hard drive.

Figure 5 Trust Ratings Eventually, we have the final trust score for each friend calculated using the trust score formula. Specifically, we use the results from the CharVector algorithm as the similarity results. Meanwhile, we assign: ρଵ ൌ ͲǤʹ, ρଶ ൌ ͲǤ͸, and ρଷ ൌ ͲǤʹ. Therefore, the final trust score for each friend is shown in Figure 6.

B. The Experimental Results

Figure 3 demonstrates the profile similarities between a person and his 10 friends, which are calculated under the four proposed algorithms.

Figure 6 Trust Scores DISCUSSION The trust computing system computes trust value between two directly connected individuals on social network. The system contains three trust computing components based on profile similarity, information V.

Figure 3 Profile Similarities Figure 4 depicts the information reliability towards each of the 10 person.

1288

reliability, and trust ratings, respectively. Suppose that there are certain individuals on a particular social network, whose profile information is always available. By only applying the similarity and the trust ratings, we are able to predict a trust-base score for any two of the individuals who have never communicated with each other. In this case, we assume that there is no information exchanging between the individuals. And either one of them may have opinions towards the other. Thus, the trust-base score for individual A towards B is formally denoted as: ”—•–ƒ•‡…‘”‡ሺȁሻ ൌ ρଵ ‫‘” כ‬ϐ‹Ž‡‹ሺǡ ሻ ൅ ρଷ ‫” כ‬୆ . We contend that the trust-base score computation is useful especially in the case of maximum trust team formation in social networks [18], where we assume that the nodes were all connected. The trust-based computational method provides a solution to the team formation problem, when the nodes were not all connected. VI.

[3]

[4] [5]

[6]

[7]

[8]

[9]

CONCLUSION

Trust, as a phenomenon in social networks, has been intensively studied on the propagations and usefulness. Little research work really focuses on computing trust score itself. In this paper, we proposed a trust computing system that simulates trust in between two connected individuals on social networks. The system is designed to model trust based on three perspectives including profile similarity, information reliability, and social opinions. Each of the perspectives is incorporated within a trust computing component, so that the system is able to compute a final trust score based on the results of the three trust computing components. Additionally, part of the system can be applied to deduce trust-base for the people who have not communicated with each other.

[10]

[11]

[12]

[13]

[14]

ACKNOWLEDGMENT This research was funded by the National Science Foundation (NSF) Science & Technology Center: Bio/computational Evolution in Action Consortium (BEACON) and National Science Foundation Award (No. HRD-1137443). The authors would like to thank the NSF for their support of this research.

[15]

[16]

[17] [1]

[2]

REFERENCES K. Walker, A. MacBride, and M. Vachon, Social Support Networks and the Crisis of Bereavement, Social Science and Medicine, 1977, vol. 2, pp. 3541. Levinger, G. (1983). Development and change. In H.H. Kelley, et al. (Eds.), Close relationships. (pp. 315–359). New York: W.H. Freeman and Company.

[18]

1289

Gambetta, D., Can We Trust Trust? In D. Gambetta, editor, Trust: Making and Breaking Cooperative Relations, pp. 213-238. Basil Blackwell. Oxford, 1990. Prosper [Available online] http://www.prosper.com/ D. Shen, C. Krumme, and A. Lippman, Follow the Profit or the Herd? Exploring Social Effects in Peerto-Peer Lending, Proceedings of The Second International Conference on Social Computing, Minneapolis, MN, USA, August 2010, pp. 137-144. G. Liu, Y. Wang, and M. Orgun, Trust Inference in Complex Trust-oriented Social Networks, Proceedings of the International Conference on Computational Science and Engineering, Vancouver, Canada, August 2009,pp. 996-1001. Lazarsfeld, P., and Merton, R., Friendship as a Social Process: A Substantive and Methodological Analysis. In Freedom and Control in Modern Society, Morroe Berger, Theodore Abel, and Charles H. Page, eds. New York: Van Nostrand, pp.18–66, 1954. McPherson, M., Smith-Lovin, L., and Cook, J., Birds of a Feather: Homophily in Social Networks, Annual Review of Sociology, vol. 27, pp. 415–44, 2001. Ziegler, C., and Golbeck, J., Investigating Interactions of Trust and Interst Similarity, Decision Support Systems, vol. 43(2), March 2007, pp. 460475. Li, J., Zhang, Z., and Zhang, W., MobiTrust: Trust Management System in Mobile Social Computing, The 10th IEEE International Conference on Computer and Information Technology, pp. 954-959, 2010. Zhan, J., and Fang, X., A Computational Trust Framework for Social Computing, , Proceedings of The Second International Conference on Social Computing, Minneapolis, MN, USA, August 2010, pp. 264-269. Josang, A., Ismail, R., and Boyd, C., A Survey of Trust and Reputation Systems for Online Service Provision, Decision Support Systems, vol. 43(2), pp. 618-644, 2007. Gruber, T., A Translation Approach to Portable Ontologies, Knowledge Acquisition, vol. 5(2), pp. 199-220, 1993. Resink, P., Using Information Content to Evaluate Semantic Similarity in a Taxonomy, Proceedings of the 14th International Joint Conference on Artificial Intelligence, 1995. Jiang, J., and Conrath, D., Semantic Similarity based on Corpus Statisticsth and Lexical Taxonomy, Proceedings of the 10 International Conference on Research on Computational Linguistics, 1997. Lin, D., An Information-Theoreticth Definition of Similarity, Proceedings of the 15 International Conference on Machine Learning, 1998. Josang, A., and Haller, J., Dirichlet Reputation Systems, Proceedings of the 2nd International Conference on Availbility, Reliability, and Security, pp. 112-119, 2007. Zhan, J., Fang, X., Killion, P., Trust Optimization in Task-Oriented Social Networks, IEEE Symposium Series on Computational Intelligence, Paris, France, April 2011.