Attention-based neural networks for trust evaluation in online social networks

Xu, Yanwei; Feng, Zhiyong; Zhou, Xian; Xing, Meng; Wu, Hongyue; Xue, Xiao; Chen, Shizhan; Wang, Chao; Qi, Lianyong

doi:10.1016/j.ins.2023.02.045

Cited by 8 publications

(6 citation statements)

References 43 publications

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“…In fact, the user's historical behavior information is dynamic and has a profound impact on social relationship prediction. For example, Xu et al [27] utilized the LSTM (Long Short-Term Memory) network to extract the overall temporal characteristics of users to further predict the trust social relationships between users, and this social relationship prediction process fully exploited and analyzed the dynamic historical behavioral information of users. Furthermore, Xu et al [28] designed an attention-based neural network model, GainTrust, by combining the users' trusted neighbors with the users' overall temporal characteristics obtained via the LSTM network to predict the trust social relationship among users.…”

Section: Social Relationship Predictionmentioning

confidence: 99%

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FSRPCL: Privacy-preserve federated social relationship prediction with contrastive learning

Liu,

Li,

Kou

et al. 2024

Tsinghua Sci. Technol.

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Cross-Platform Social Relationship Prediction (CPSRP) aims to utilize users' data information on multiple platforms to enhance the performance of social relationship prediction, thereby promoting socioeconomic development. Due to the highly sensitive nature of users' data in terms of privacy, CPSRP typically introduces various privacy-preserving mechanisms to safeguard users' confidential information. Although the introduction mechanism guarantees the security of the users' private information, it tends to degrade the performance of the social relationship prediction. Additionally, existing social relationship prediction schemes overlook the interdependencies among items invoked in a user behavior sequence. For this purpose, we propose a novel privacy-preserve Federated Social Relationship Prediction with Contrastive Learning framework called FSRPCL, which is a multi-task learning framework based on vertical federated learning. Specifically, the users' rating information is perturbed with a bounded differential privacy technology, and then the users' sequential representation information acquired through Transformer is applied for social relationship prediction and contrastive learning. Furthermore, each client uploads their respective weight information to the server, and the server aggregates the weight information and distributes it purposes to each client for updating.Numerous experiments on real-world datasets prove that FSRPCL delivers exceptional performance in social relationship prediction and privacy preservation, and effectively minimizes the impact of privacy-preserving technology on social relationship prediction accuracy.

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Section: Social Relationship Predictionmentioning

confidence: 99%

“…e. MemTrust [27] : MemTrust mainly applies Longand Short-Term Memory and MLP networks to extract time-series features of users on a single platform, and further predicts the trust social relationships among users.…”

Section: Experimental Environmentmentioning

confidence: 99%

FSRPCL: Privacy-preserve federated social relationship prediction with contrastive learning

Liu,

Li,

Kou

et al. 2024

Tsinghua Sci. Technol.

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“…The authors designed an attention-based neural network model for the goal of predicting trust relationships. 24 In the last few years, the issue of traffic offloading in UAV-assisted networks, in particular, has received a lot of attention. The authors introduced a novel dynamic traffic congestion pricing and electric vehicle charging management system in an urban smart city environment.…”

Section: Related Workmentioning

confidence: 99%

“…The authors designed a comprehensive performance model for task offloading in a fog computing environment by using network calculus theory 22 . Furthermore, the offloading approach 23 and task prediction 24 within the context of neural networks are investigated. In the dynamic and heterogeneous edge‐cloud environment, 23 the authors proposed a dynamic offloading approach based on game theory in conjunction with convolutional neural network partition.…”

Section: Related Workmentioning

confidence: 99%

“…In the dynamic and heterogeneous edge‐cloud environment, 23 the authors proposed a dynamic offloading approach based on game theory in conjunction with convolutional neural network partition. The authors designed an attention‐based neural network model for the goal of predicting trust relationships 24 . In the last few years, the issue of traffic offloading in UAV‐assisted networks, in particular, has received a lot of attention.The authors introduced a novel dynamic traffic congestion pricing and electric vehicle charging management system in an urban smart city environment 25 .…”

Section: Related Workmentioning

confidence: 99%

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Traffic offloading for postdisaster rescue in UAV‐assisted networks: A coalition formation game approach

Qiu,

Huang,

et al. 2023

Concurrency and Computation

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SummaryIn postdisaster rescue scenario, unmanned aerial vehicles (UAVs) are effective tools to help ground users in disaster areas to transmit rescue‐critical data to the relief center in time due to their flexible mobilities and fast deployment. However, how UAVs choose disaster areas to take part in traffic offloading with limited bandwidth resources is challenging. This article investigates UAVs' access selection and disaster areas' bandwidth allocation scheme by jointly optimizing network throughput, bandwidth and UAVs' energy cost. The UAVs form coalitions to participate in traffic offloading cooperatively, where each UAV selects a disaster area independently. Specifically, the bandwidth resources are dynamically allocated to all disaster areas when a new coalition partition forms. The UAVs adjust their access choices for disaster areas when the bandwidth allocation changes. When no UAVs migrate to other disaster areas, the network achieves a stable state. The UAVs fly to the final selected disaster area and provide traffic offloading services. To resolve traffic offloading competitions yet enable cooperations among UAVs, we address the UAVs' access selection issue by coalition formation game. A gradient projection method is then proposed to allocate bandwidth resources which maximizes the benefit of the network. We demonstrate that the UAVs' access selection and disaster areas' bandwidth allocation algorithms are convergent. The simulation results demonstrate that our proposed cooperation order is better than the pareto and selfish orders, thereby increasing the benefit of the network.

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Metapath-guided multi-headed attention networks for trust prediction in heterogeneous social networks

Xu,

Feng,

Xing

et al. 2023

Knowledge-Based Systems

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Attention-based neural networks for trust evaluation in online social networks

Cited by 8 publications

References 43 publications

FSRPCL: Privacy-preserve federated social relationship prediction with contrastive learning

FSRPCL: Privacy-preserve federated social relationship prediction with contrastive learning

Traffic offloading for postdisaster rescue in UAV‐assisted networks: A coalition formation game approach

Metapath-guided multi-headed attention networks for trust prediction in heterogeneous social networks

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