Continuous Graph Neural NetworksLouis-Pascal XhonneuxMeng QuJian TangInternational Conference on Machine Learning
Keywords:High-pass Filtering,Graph Neural Networks Abstract 近年来,连续图神经网络(CGNNs)由于其无限深度而不需要过度平滑的优点而引起了人们的广泛关注。然而,大多数现有的cgnn在本质上执行低通滤波,因为它们是基于离散拉普拉斯平滑的图神经网络(GNNs)。虽然先前的研究表明,高通滤波对节点表示学习有很好的结果,特别是...
From diffusion equations to graph neural networks 图神经网络的一个基本构建块是信息传递,其中信息在相邻节点之间流动。信息传递与扩散方程有自然的联系,扩散方程描述了某些感兴趣的数量,如质量或热量,如何作为时间的函数在空间上分散。 Diffusion equation in continuous domains.主导任何扩散方程的两个定律是菲克定律和...
Continuous graph neural networks (CGNNs) have garnered significant attention due to their ability to generalize existing discrete graph neural networks (GNNs) by introducing continuous dynamics. They typically draw inspiration from diffusion-based methods to introduce a novel propagation scheme, which is...
@misc{xhonneux2019continuous, title={Continuous Graph Neural Networks}, author={Louis-Pascal A. C. Xhonneux and Meng Qu and Jian Tang}, year={2019}, eprint={1912.00967}, archivePrefix={arXiv}, primaryClass={cs.LG} } About No description, website, or topics provided. Resources Readme...
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Technology Keyword: Graph Neural Network, Graph convolutional network, Graph network, Graph attention network, Graph auto-encoder,... Very hot research topic: the representative work--Graph convolutional networks (GCNs) proposed by T.N. Kipf and M. Welling (ICLR2017 [5] in conference paper ...
To achieve this, we propose a principled graph-neural-based approach to learn continuous-time dynamic embeddings. We first define a temporal dependency interaction graph(TDIG) that is induced from sequences of interaction data. Based on the topology of this TDIG, we develop a dynamic message ...
This adjacency matrix can have an arbitrary sparsity (that is, there is no need to use a directed acyclic graph for the coupling between neurons). Algorithm 1 Translate the architecture of an LTC network into its closed-form variant Inputs: LTC inputs I(N×T)(t), the activity x(H...
EEG-based emotion recognition using regularized graph neural networks. IEEE Trans. Affect. Comput. 2020, 13, 1290–1301. [Google Scholar] [CrossRef] Bhardwaj, A.; Gupta, A.; Jain, P.; Rani, A.; Yadav, J. Classification of human emotions from EEG signals using SVM and LDA Classifiers....