Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation

Guo, Naicheng; Li, Shaoshuai; Ma, Qiongxu; Gao, Ke; Han, Bing; Zheng, Lin; Guo, Shouwu; Guo, Xiaobo

doi:10.1145/3568395

Cited by 8 publications

(1 citation statement)

References 29 publications

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“…They demonstrate superior performance accuracy on complex synthetic datasets and real-world datasets. Guo et al 41 proposed a Poincaré-based heterogeneous graph neural network for sequential recommendation, which models both sequential pattern information and hierarchical information.…”

Section: Hyperbolic Spacementioning

confidence: 99%

Vison transformer adapter-based hyperbolic embeddings for multi-lesion segmentation in diabetic retinopathy

Wang

Yan

et al. 2023

Sci Rep

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Diabetic Retinopathy (DR) is a major cause of blindness worldwide. Early detection and treatment are crucial to prevent vision loss, making accurate and timely diagnosis critical. Deep learning technology has shown promise in the automated diagnosis of DR, and in particular, multi-lesion segmentation tasks. In this paper, we propose a novel Transformer-based model for DR segmentation that incorporates hyperbolic embeddings and a spatial prior module. The proposed model is primarily built on a traditional Vision Transformer encoder and further enhanced by incorporating a spatial prior module for image convolution and feature continuity, followed by feature interaction processing using the spatial feature injector and extractor. Hyperbolic embeddings are used to classify feature matrices from the model at the pixel level. We evaluated the proposed model’s performance on the publicly available datasets and compared it with other widely used DR segmentation models. The results show that our model outperforms these widely used DR segmentation models. The incorporation of hyperbolic embeddings and a spatial prior module into the Vision Transformer-based model significantly improves the accuracy of DR segmentation. The hyperbolic embeddings enable us to better capture the underlying geometric structure of the feature matrices, which is important for accurate segmentation. The spatial prior module improves the continuity of the features and helps to better distinguish between lesions and normal tissues. Overall, our proposed model has potential for clinical use in automated DR diagnosis, improving accuracy and speed of diagnosis. Our study shows that the integration of hyperbolic embeddings and a spatial prior module with a Vision Transformer-based model improves the performance of DR segmentation models. Future research can explore the application of our model to other medical imaging tasks, as well as further optimization and validation in real-world clinical settings.

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Section: Hyperbolic Spacementioning

confidence: 99%

Vison transformer adapter-based hyperbolic embeddings for multi-lesion segmentation in diabetic retinopathy

Wang

Yan

et al. 2023

Sci Rep

View full text Add to dashboard Cite

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EDGCNet: Joint dynamic hyperbolic graph convolution and dual squeeze-and-attention for 3D point cloud segmentation

Cheng,

Zhu,

et al. 2024

Expert Systems with Applications

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Privacy-Preserving Individual-Level COVID-19 Infection Prediction via Federated Graph Learning

Fu,

Wang,

Gao

et al. 2024

ACM Trans. Inf. Syst.

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Accurately predicting individual-level infection state is of great value since its essential role in reducing the damage of the epidemic. However, there exists an inescapable risk of privacy leakage in the fine-grained user mobility trajectories required by individual-level infection prediction. In this paper, we focus on developing a framework of privacy-preserving individual-level infection prediction based on federated learning (FL) and graph neural networks (GNN). We propose Falcon , a F ederated gr A ph L earning method for privacy-preserving individual-level infe C tion predicti ON . It utilizes a novel hypergraph structure with spatio-temporal hyperedges to describe the complex interactions between individuals and locations in the contagion process. By organically combining the FL framework with hypergraph neural networks, the information propagation process of the graph machine learning is able to be divided into two stages distributed on the server and the clients, respectively, so as to effectively protect user privacy while transmitting high-level information. Furthermore, it elaborately designs a differential privacy perturbation mechanism as well as a plausible pseudo location generation approach to preserve user privacy in the graph structure. Besides, it introduces a cooperative coupling mechanism between the individual-level prediction model and an additional region-level model to mitigate the detrimental impacts caused by the injected obfuscation mechanisms. Extensive experimental results show that our methodology outperforms state-of-the-art algorithms and is able to protect user privacy against actual privacy attacks. Our code and datasets are available at the link: https://github.com/wjfu99/FL-epidemic.

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Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation

Cited by 8 publications

References 29 publications

Vison transformer adapter-based hyperbolic embeddings for multi-lesion segmentation in diabetic retinopathy

Vison transformer adapter-based hyperbolic embeddings for multi-lesion segmentation in diabetic retinopathy

EDGCNet: Joint dynamic hyperbolic graph convolution and dual squeeze-and-attention for 3D point cloud segmentation

Privacy-Preserving Individual-Level COVID-19 Infection Prediction via Federated Graph Learning

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