Gated residual recurrent graph neural networks for traffic prediction. Resource Allocation (A GNN-Based Federated Learning Framework for Resource Allocation in Wireless IoT Networks) A GNN-Based Federated Learning Framework for Resource Allocation in Wireless IoT Networks 借助于图的建模,无线物联网系统由图...
Graph Neural Networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art results in numerous IoT learning tasks. In this survey, we present a comprehensive review of ...
Based on graph theory, a number of enhanced GNNs are proposed to deal with non-Euclidean datasets. In this study, we first review the artificial neural networks and GNNs. We then present ways to extend deep learning models to deal with datasets in non-Euclidean space and introduce the G...
在过去的几年里,图神经网络(Graph Neural Networks, GNN)已经成为人工智能领域的一个热门话题,它代表了一种在图数据上进行学习和推理的强大工具。随着数据量的爆炸式增长,尤其是结构化数据,在图形表示中捕获实体间复杂关系的需求日益增加。GNN的出现,为解决这一挑战提供了新的视角和方法。 本系列文章从GNN的基础概念...
A Survey on Graph Neural Networks in Intelligent Transportation Systems[J]. arXiv preprint arXiv:2401.00713, 2024. Link 2023 Journal Qi X, Yao J, Wang P, et al. Combining weather factors to predict traffic flow: A spatial‐temporal fusion graph convolutional network‐based deep learning ...
Road speed prediction, which is a sub-task of traffic flow forecasting, is challenging due to the complicated spatial dependencies characterizing road networks and dynamic temporal traffic patterns. Given the power of recurrent neural networks (RNNs) in learning temporal relations and graph neural ...
To enable learning on large-dimensional and complex data, specific neural network architectures have been developed, including convolutional and graph neural networks. In this work, we present a novel encoder–decoder geometric deep learning framework called MAgNET, which extends the well-known ...
Graph neural networks in IoT: A survey. ACM Trans. Sens. Netw. 2023, 19, 1–50. [Google Scholar] [CrossRef] Jia, M.; Gabrys, B.; Musial, K. A Network Science perspective of Graph Convolutional Networks: A survey. IEEE Access 2023. [Google Scholar] [CrossRef] Ren, H.; Lu, W....
A Targeted Universal Attack on Graph Convolutional Network, 📝arXiv, Code Query-free Black-box Adversarial Attacks on Graphs, 📝arXiv Reinforcement Learning-based Black-Box Evasion Attacks to Link Prediction in Dynamic Graphs, 📝arXiv Efficient Evasion Attacks to Graph Neural Networks via Influen...
1. Survey Papers A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications (coming soon!). authors from NDS Lab and ORNL. GNN Surveys (to be added). Privacy in ML (to be added). 2. GNN Privacy Attack Papers 2.1 Membership Inference Attack Membership Inference ...