The experimental results show that the performance of the LGNN algorithm in some tasks is slightly worse than that of the existing mainstream graph neural network algorithms, but it shows or exceeds ...

Abstract: This paper proposes novel architectures for spatio-temporal graph convolutional and recurrent neural networks whose structure is inspired ... In both cases the physics based GCN and GRN ...

In this paper, we propose a graph neural network algorithm that leverages graph convolution and the attention mechanism to enhance the expressive power and aggregation efficacy of node features within ...

Developing sophisticated algorithms for representation learning on graph structured data holds significant research value as it enables smoother execution of subsequent tasks. Graph Neural Networks ...

Since in most of the datasets we find that structural relationship between the entities of data we can use the graph neural networks in place of other ML algorithms and can utilize the benefits of ...

Recently, graph neural networks have been successfully applied to graph structured ... These modules are combined into a layer, and the layers can be stacked together into an algorithm. We show that ...

Our starting point is previous work on Graph Neural Networks (Scarselli et al ... We demonstrate the capabilities on some simple AI (bAbI) and graph algorithm learning tasks. We then show it achieves ...

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