Evolution of trust networks in social web applications using supervised learning

Zolfaghar, Kiyana; Aghaie, Abdollah

doi:10.1016/j.procs.2010.12.137

Cited by 29 publications

(20 citation statements)

References 8 publications

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“…One is to use supervised learning. The selected training data is related to time series [Huang et al 2005;Imana et al 2010;Zolfaghar and Aghaie 2011;Yuan et al 2006]. The other is to use reinforcement learning, which itself continually optimizes strategies based on real-time feedback from performing operations [Kim and Song 2011;Tran 2015, 2017].…”

Section: Discussionmentioning

confidence: 99%

A Survey on Trust Evaluation Based on Machine Learning

et al. 2020

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Trust evaluation is the process of quantifying trust with attributes that influence trust. It faces a number of severe issues such as lack of essential evaluation data, demand of big data process, request of simple trust relationship expression, and expectation of automation. In order to overcome these problems and intelligently and automatically evaluate trust, machine learning has been applied into trust evaluation. Researchers have proposed many methods to use machine learning for trust evaluation. However, the literature still lacks a comprehensive literature review on this topic. In this article, we perform a thorough survey on trust evaluation based on machine learning. First, we cover essential prerequisites of trust evaluation and machine learning. Then, we justify a number of requirements that a sound trust evaluation method should satisfy, and propose them as evaluation criteria to assess the performance of trust evaluation methods. Furthermore, we systematically organize existing methods according to application scenarios and provide a comprehensive literature review on trust evaluation from the perspective of machine learning's function in trust evaluation and evaluation granularity. Finally, according to the completed review and evaluation, we explore some open research problems and suggest the directions that are worth our research effort in the future.

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Section: Discussionmentioning

confidence: 99%

A Survey on Trust Evaluation Based on Machine Learning

et al. 2020

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“…Zolfaghar and Aghaie [72] developed a supervised timeaware trust prediction approach. They considered the trust prediction problem as a temporal link prediction problem.…”

Section: ) Supervised Approachesmentioning

confidence: 99%

“…Supervised/Unsupervised Context-Aware Dynamic Moradi and Ahmadian [88] U N Y Sanadhy and Singh [89] U N Y Raj and Babu [73] U N N Zhao et al [74] S Y N Zhang et al [93] U Y Y Zhang et al [107] S Y N Zhang et al [75] S N N Zhang et al [108] S Y N Liu et al [109] U Y N Zheng et al [112] U Y N Matsutani et al [94] U N N Tang et al [95] U N Y Zhang and Yu [47] U N N Chakraverty et al [76] S N N Sacco and Breslin [56] S N N Huang et al [96] U N N Li and Wang [117] U Y N Fazeli et al [90] U N N Tang et al [92] U N N Moturu and Liu [97] U N N Nunez-Gonzalez et al [77] S N N Yao et al [98] U N N Huang et al [99] U N N Liu et al [37] S N Y Ma et alL [60] S N N Matsuo and Yamamoto [61] S N Y Grana et al [62] S N N Wang et al [63] S N N Bachi et al [66] S Y N Korovaiko and Thomo [68] S N N Borzymek and Sydow [69] S N N Laspez and Maag [70] S Y N Ghafari et al [65] S Y N Zolfaghar and Aghaie [72] S Y Y Tang et al [25] U N N Wang et al [82] U N N Ghafari et al [58] and [4] U Y N Guha et al [84] U N N Golbeck [85] U N N Wang et al [32] U N N Zheng et al …”

Section: Approachmentioning

confidence: 99%

A Survey on Trust Prediction in Online Social Networks

et al. 2020

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Level of Trust can determine which source of information is reliable and with whom we should share or from whom we should accept information. There are several applications for measuring trust in Online Social Networks (OSNs), including social spammer detection, fake news detection, retweet behaviour detection and recommender systems. Trust prediction is the process of predicting a new trust relation between two users who are not currently connected. In applications of trust, trust relations among users need to be predicted. This process faces many challenges, such as the sparsity of user-specified trust relations, the context-awareness of trust and changes in trust values over time. In this paper, we analyse the state-of-the-art in pair-wise trust prediction models in OSNs, classify them based on different factors, and propose some future directions for researchers interested in this field.

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“…Authors adopted the Rigg's algorithm to compute quality of service content and the user's trust degree of providing content because the clarity trust rank for credible websites is not always useful and is typically sparse. Zolfaghar and Abdollah (2011) proposed evolution of trust networks in social web applications using supervised learning. In order to predict the probability of trust relationship, the paper maps the current issue on formal link prediction problem and solves it with supervised learning.…”

Section: Relate Workmentioning

confidence: 99%

Dynamic trust evaluation model based on bidding and multi-attributes for social networks

Wang

Park

Sandhu

et al. 2019

IJHPCN

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Mutual trust is the most important basis in social networks. However, many malicious nodes often deceive, collaboratively cheat, and maliciously recommend other nodes for getting the more benefits. Meanwhile, because of lacking effective incentive strategy, many nodes are neither to evaluate nor to recommend. Thus, malicious actions have been aggravated in social networks. To solve these issues, we designed a bidding strategy to incentivise nodes to do their best to recommend or evaluate service node. At the same time, we also employed TOPSIS method of selecting a correct service node for system from networks. To guarantee reliability of service node selected, we brought recommendation time influential function, service content similarity function and recommendation acquaintance function into the model to compute general trust of node. Finally, we gave an update method for trust degree of node and experiments analysis.

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Evolution of trust networks in social web applications using supervised learning

Cited by 29 publications

References 8 publications

A Survey on Trust Evaluation Based on Machine Learning

A Survey on Trust Evaluation Based on Machine Learning

A Survey on Trust Prediction in Online Social Networks

Dynamic trust evaluation model based on bidding and multi-attributes for social networks

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