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<h1><a href="https://nn.labml.ai/graphs/gat/index.html">图表注意力网络 (GAT)</a></h1>
<p>这是 <a href="https://pytorch.org">PyTorch</a> 对《<a href="https://arxiv.org/abs/1710.10903">图形注意力网络</a>》论文的实现。</p>
<p>GAT 处理图表数据。图由节点和连接节点的边组成。例如，在 Cora 数据集中，节点是研究论文，边缘是连接论文的引文。</p>
<p>GAT 使用蒙面自注意力，有点类似于<a href="https://nn.labml.ai/transformers/mha.html">变形金刚</a>。GAT 由相互堆叠的图表注意力层组成。每个图注意力层都将节点嵌入作为转换后的嵌入的输入和输出获得节点。节点嵌入会注意它所连接的其他节点的嵌入。图形注意力层的详细信息与实现一起包括在内。</p>
<p>以下是<a href="https://nn.labml.ai/graphs/gat/experiment.html">在 Cora 数据集上训练两层 GAT 的训练代码</a>。</p>
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