Attention is all you need for boosting graph convolutional neural network

arXiv:2403.15419v1 Announce Type: new
Abstract: Graph Convolutional Neural Networks (GCNs) possess strong capabilities for processing graph data in non-grid domains. They can capture the topological logical structure and node features in graphs and integrate them into nodes' final representations. GCNs have been extensively studied in various fields, such as recommendation systems, social networks, and protein molecular structures. With the increasing application of graph neural networks, research has focused on improving their performance while compressing their size. In this work, a …

abstract arxiv attention attention is all you need boosting capabilities convolutional neural network convolutional neural networks cs.gr cs.lg cs.si data domains features fields graph graph data graphs grid network networks neural network neural networks node nodes processing them type