论文标题
重新访问图形神经网络的嵌入
Revisiting Embeddings for Graph Neural Networks
论文作者
论文摘要
当前的图形表示技术使用图形神经网络（GNN）从数据集嵌入中提取特征。在这项工作中，我们检查了这些嵌入的质量，并评估改变它们如何影响GNN的准确性。我们探索图像和文本的不同嵌入提取技术。并发现不同GNN体系结构的性能取决于所使用的嵌入方式。我们看到可用图形数据集中的单词嵌入和文本分类任务的普遍存在。鉴于嵌入对GNN性能的影响。这导致了一种现象，该现象是针对弓向量优化的。
Current graph representation learning techniques use Graph Neural Networks (GNNs) to extract features from dataset embeddings. In this work, we examine the quality of these embeddings and assess how changing them can affect the accuracy of GNNs. We explore different embedding extraction techniques for both images and texts; and find that the performance of different GNN architectures is dependent on the embedding style used. We see a prevalence of bag of words (BoW) embeddings and text classification tasks in available graph datasets. Given the impact embeddings has on GNN performance. this leads to a phenomenon that GNNs being optimised for BoW vectors.
