论文标题
边缘熵作为GNNs对CNN的有效性的指标
Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification
论文作者
论文摘要
图形神经网络（GNN）将卷积神经网络（CNN）扩展到基于图的数据。出现的一个问题是，GNN中的基础图结构在CNN上提供了多少（忽略此图结构）。为了解决这个问题，我们介绍了边缘熵，并评估指标在CNN上可能提高GNN的性能是多么好。我们对合成和真实数据集的节点分类的结果表明，较低的边熵值可以预测GNN在CNN上的预期性能较大，相反，更高的边熵会导致预期的较小的改进增长。
Graph neural networks (GNNs) extend convolutional neural networks (CNNs) to graph-based data. A question that arises is how much performance improvement does the underlying graph structure in the GNN provide over the CNN (that ignores this graph structure). To address this question, we introduce edge entropy and evaluate how good an indicator it is for possible performance improvement of GNNs over CNNs. Our results on node classification with synthetic and real datasets show that lower values of edge entropy predict larger expected performance gains of GNNs over CNNs, and, conversely, higher edge entropy leads to expected smaller improvement gains.
