TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature

• URL: http://arxiv.org/abs/2205.12784v1
• Date: Wed, 25 May 2022 13:57:03 GMT
• Title: TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature
• Authors: Cuiying Huo, Di Jin, Chundong Liang, Dongxiao He, Tie Qiu and Lingfei Wu
• Abstract summary: Trust evaluation is critical for many applications such as cyber security, social communication and recommender systems. We propose a new GNN based trust evaluation method named TrustGNN, which integrates smartly the propagative and composable nature of trust graphs. Specifically, TrustGNN designs specific propagative patterns for different propagative processes of trust, and distinguishes the contribution of different propagative processes to create new trust.
• Score: 63.78619502896071
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trust evaluation is critical for many applications such as cyber security, social communication and recommender systems. Users and trust relationships among them can be seen as a graph. Graph neural networks (GNNs) show their powerful ability for analyzing graph-structural data. Very recently, existing work attempted to introduce the attributes and asymmetry of edges into GNNs for trust evaluation, while failed to capture some essential properties (e.g., the propagative and composable nature) of trust graphs. In this work, we propose a new GNN based trust evaluation method named TrustGNN, which integrates smartly the propagative and composable nature of trust graphs into a GNN framework for better trust evaluation. Specifically, TrustGNN designs specific propagative patterns for different propagative processes of trust, and distinguishes the contribution of different propagative processes to create new trust. Thus, TrustGNN can learn comprehensive node embeddings and predict trust relationships based on these embeddings. Experiments on some widely-used real-world datasets indicate that TrustGNN significantly outperforms the state-of-the-art methods. We further perform analytical experiments to demonstrate the effectiveness of the key designs in TrustGNN.

Related papers

• Uncertainty in Graph Neural Networks: A Survey [50.63474656037679]
  Graph Neural Networks (GNNs) have been extensively used in various real-world applications. However, the predictive uncertainty of GNNs stemming from diverse sources can lead to unstable and erroneous predictions. This survey aims to provide a comprehensive overview of the GNNs from the perspective of uncertainty.
  arXiv  Detail & Related papers  (2024-03-11T21:54:52Z)
• TrustGuard: GNN-based Robust and Explainable Trust Evaluation with Dynamicity Support [59.41529066449414]
  We propose TrustGuard, a GNN-based accurate trust evaluation model that supports trust dynamicity. TrustGuard is designed with a layered architecture that contains a snapshot input layer, a spatial aggregation layer, a temporal aggregation layer, and a prediction layer. Experiments show that TrustGuard outperforms state-of-the-art GNN-based trust evaluation models with respect to trust prediction across single-timeslot and multi-timeslot.
  arXiv  Detail & Related papers  (2023-06-23T07:39:12Z)
• KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks [63.531790269009704]
  Social Internet of Things (SIoT) is a promising and emerging paradigm that injects the notion of social networking into smart objects (i.e., things) Due to the risks and uncertainty, a crucial and urgent problem to be settled is establishing reliable relationships within SIoT, that is, trust evaluation. We propose a novel knowledge-enhanced graph neural network (KGTrust) for better trust evaluation in SIoT.
  arXiv  Detail & Related papers  (2023-02-22T14:24:45Z)
• An Evolutionary Game based Secure Clustering Protocol with Fuzzy Trust Evaluation and Outlier Detection for Wireless Sensor Networks [8.611020067829509]
  A fuzzy trust evaluation method is presented to transform the transmission evidences into trust values. A K-Means based outlier detection scheme is proposed to further analyze plenty of trust values. An evolutionary game based secure clustering protocol is presented to achieve a trade-off between security assurance and energy saving.
  arXiv  Detail & Related papers  (2022-07-21T03:24:35Z)
• Trustworthy Graph Neural Networks: Aspects, Methods and Trends [115.84291569988748]
  Graph neural networks (GNNs) have emerged as competent graph learning methods for diverse real-world scenarios. Performance-oriented GNNs have exhibited potential adverse effects like vulnerability to adversarial attacks. To avoid these unintentional harms, it is necessary to build competent GNNs characterised by trustworthiness.
  arXiv  Detail & Related papers  (2022-05-16T02:21:09Z)
• A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability [59.80140875337769]
  Graph Neural Networks (GNNs) have made rapid developments in the recent years. GNNs can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data. This paper gives a comprehensive survey of GNNs in the computational aspects of privacy, robustness, fairness, and explainability.
  arXiv  Detail & Related papers  (2022-04-18T21:41:07Z)
• Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration [32.26725705900001]
  Despite Graph Neural Networks (GNNs) having remarkable accuracy, whether the results are trustworthy is still unexplored. Previous studies suggest that many modern neural networks are over-confident on the predictions. We propose a novel trustworthy GNN model by designing a topology-aware post-hoc calibration function.
  arXiv  Detail & Related papers  (2021-09-29T09:08:20Z)
• Towards Time-Aware Context-Aware Deep Trust Prediction in Online Social Networks [0.4061135251278187]
  Trust can be defined as a measure to 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 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.
  arXiv  Detail & Related papers  (2020-03-21T01:00:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.