Trust Computation in Online Social Networks Using Co-Citation and Transpose Trust Propagation

Nasir, Safi Ullah; Kim, Tae-Hyung

doi:10.1109/access.2020.2975782

Cited by 13 publications

(8 citation statements)

References 16 publications

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“…In [8], trust is quantified as a continuous and realvalued degree between two unidentified users. This trust computation method takes into account co-citation and transpose trust.…”

Section: Related Workmentioning

confidence: 99%

User Activities Based Trust Management Framework for Online Social Networks

Sirisala,

Sirisala

2024

Preprint

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The ubiquity of mobile phones and web technology has facilitated continuous communication within Online Social Networks (OSNs) such as Twitter, Instagram, Facebook, and LinkedIn. In OSNs, individuals have the capability to share posts and photos with their relatives and friends, as well as to promote and publicize their businesses. However, this also opens up the potential for malicious users to misuse personal information. Consequently, researchers have turned their attention toward proposing trust-related methods aimed at ensuring a secure environment within social networks. Trust management involves the observation and rating of each user based on his activities, ensuring that only trustworthy users are granted permission to share their activities. In this paper “User Activities Based Trust Management Framework for Online Social Networks-TMFOSN” is proposed. Where users trust values are managed and keep getting updated through the different phases like trust computation, aggregation, propagation, and updations. In TMFOSN, user direct and indirect trust is evaluated with reference of Twitter social network dataset. The performance of TMFOSN is compared with other trust estimation methods, where it is outperformed with increased number of user profiles.

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“…In [8], trust is quantified as a continuous and realvalued degree between two unidentified users. This trust computation method takes into account co-citation and transpose trust.…”

Section: Related Workmentioning

confidence: 99%

User Activities Based Trust Management Framework for Online Social Networks

Sirisala,

Sirisala

2024

Preprint

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“…In Reference 16, the real and continuous trust value is computed for unconnected two users. Here the trust is computed based on co‐citation and transpose trust propagation.…”

Section: Related Workmentioning

confidence: 99%

A novel trust recommendation model in online social networks using soft computing methods

Sirisala¹,

Yarava

Reddy³

et al. 2022

Concurrency and Computation

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Summary Social network (OSN) is an emerging platform through which people can connect with their friends, relatives, and other like‐minded people. On the other hand, users' personal information might be misused because of other users' biased and malicious behavior. Establishing a trusted environment in social networks is one of the current research problems. Some of the research papers proposed to trust computational methods, but still, there is a lack of methods to handle biased recommendations and loss of trust accuracy towards the target user. In this article, to address these open issues, “a novel trust recommendation model in online social networks using soft computing methods (TRMSC)” is proposed for the Twitter social networks. Here direct and indirect trust is computed for known and unknown users, respectively. The direct trust of a user is computed using clustering methods based on his social activities (posts, retweets received, mentions received, listed count, and follower count) with other users. In the computation of indirect trust, the impact of biased recommendations is suppressed using the Dempster Shafer theory(DST) method, and loss of trust is minimized using trust transitive matrices. The performance of the proposed method is analyzed theoretically and experimentally. Time and space complexities are measured using asymptotic notations. In experimental results, TRMSC is evaluated for different network sizes and for target users at different distances (2 to 4‐hops), where it could perform better than existing methods.

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“…Here the indirect trust is evaluated for the target user at 3‐hop distance from source user. Performance of proposed method TMFPN is compared with existing trust prediction methods like TCCTP 27 and TMUAB 28 . With increment of users in the social network, proposed method could find the multiple trustworthy paths towards the target user.…”

Section: Evaluation Of the Proposed Model For Calculating Trust On Tw...mentioning

confidence: 99%

An efficient trust inference model in online social networks using fuzzy petri nets

Yarava

Bindu

2022

Concurrency and Computation

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Summary Sharing and exchanging of information among the group of users is the objective of social networks. With the growing popularity of the internet applications, many of the interactions are among the unknown users. Estimation of the user trustworthiness prior to conversation with him can increase the confidence level of a person in taking right decisions. Facebook, Twitter, LinkedIn, Instagram and Google+ are some of the most popular social networks, where the similar kinds of users share their messages and photos. In this article, the proposed method “Trust Inference Model in Online Social Networks Using Fuzzy Petri Nets (TMFPN)” computes the user trust value through two phases, that is, direct and indirect using twitter user data set. The direct trust of a user is evaluated based on his social activities like (follower count, listed count, mentions received, re tweets received, and posts) using fuzzy inference model. For the users those are not in direct interaction, indirect trust is computed. Proposed method‐TMFPN gathers network information through trust propagation and models the social network as a fuzzy petri net. Where users and interactions are considered as places and transitions, respectively. Here the concurrent reasoning algorithm (CRA) is applied over the converted FPN, for the selection of reliable trustworthy paths between two users at multiple hops distance. Performance of the proposed method is verified theoretically and practically. In the experiment results, performance of TMFPN is compared with existing trust computing methods in computation of indirect trust of multihop away users.

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Trust Computation in Online Social Networks Using Co-Citation and Transpose Trust Propagation

Cited by 13 publications

References 16 publications

User Activities Based Trust Management Framework for Online Social Networks

User Activities Based Trust Management Framework for Online Social Networks

A novel trust recommendation model in online social networks using soft computing methods

An efficient trust inference model in online social networks using fuzzy petri nets

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