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Bridging the Gap Between Spectral and Spatial Domainsin Graph Neural Networks
Bridging the Gap Between Spectral and Spatial Domainsin Graph Neural Net...
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A Comprehensive Survey on Graph Neural Networks
Deep learning has revolutionized many machine learning tasks in recent y...
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Understanding attention in graph neural networks
We aim to better understand attention over nodes in graph neural network...
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Conditional Graph Neural Processes: A Functional Autoencoder Approach
We introduce a novel encoder-decoder architecture to embed functional pr...
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Distance-Preserving Graph Embeddings from Random Neural Features
We present Graph Random Neural Features (GRNF), a novel embedding method...
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Machine Learning on Graphs: A Model and Comprehensive Taxonomy
There has been a surge of recent interest in learning representations fo...
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Learning Representations of Graph Data -- A Survey
Deep Neural Networks have shown tremendous success in the area of object...
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Graph Neural Networks: Taxonomy, Advances and Trends
Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-dimensional spaces according to specific tasks. Up to now, there have been several surveys on this topic. However, they usually lay emphasis on different angles so that the readers can not see a panorama of the graph neural networks. This survey aims to overcome this limitation, and provide a comprehensive review on the graph neural networks. First of all, we provide a novel taxonomy for the graph neural networks, and then refer to up to 400 relevant literatures to show the panorama of the graph neural networks. All of them are classified into the corresponding categories. In order to drive the graph neural networks into a new stage, we summarize four future research directions so as to overcome the facing challenges. It is expected that more and more scholars can understand and exploit the graph neural networks, and use them in their research community.
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