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 ...

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 ...

In order to overcome this defect, a graph convolution neural network algorithm based on rough graph is proposed in this paper. Specifically, the algorithm first constructs a rough graph using a ...

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 ...

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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 ...

The course will teach students topics such as representation learning and graph neural networks, algorithms for the World Wide Web, and reasoning over Knowledge Graphs. It will also cover areas ...