Graph Regression and Classification using Permutation Invariant Representations
Graph Regression and Classification using Permutation Invariant Representations
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发表时间：
2021
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通讯作者：
Naveed Haghani;Maneesh Singh;R. Balan
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作者：
Naveed Haghani;Maneesh Singh;R. Balan
我们使用图卷积神经网络和置换不变表示来解决图回归问题。许多图神经网络算法可以抽象为节点之间的一系列消息传递函数，最终为每个节点产生一组潜在特征。处理这些潜在特征以在整个图上产生单个估计取决于节点在图的表示中的排序方式。我们提出了一个置换不变的映射，产生的图形表示是不变的任何顺序的节点。这种映射可以作为利用图卷积网络进行图分类和图回归问题的关键部分。我们测试了这种方法，并在QM9数据集上验证了我们的解决方案。
We address the problem of graph regression using graph convolutional neural networks and permutation invariant representation. Many graph neural network algorithms can be abstracted as a series of message passing functions between the nodes, ultimately producing a set of latent features for each node. Processing these latent features to produce a single estimate over the entire graph is dependent on how the nodes are ordered in the graph’s representation. We propose a permutation invariant mapping that produces graph representations that are invariant to any ordering of the nodes. This mapping can serve as a pivotal piece in leveraging graph convolutional networks for graph classification and graph regression problems. We tested out this method and validated our solution on the QM9 dataset.