论文标题
通过图形群集簇分类移动射击节点分类
Shift-Robust Node Classification via Graph Adversarial Clustering
论文作者
论文摘要
图形神经网络（GNN）是图形结构化数据中事实上的节点分类模型。但是，在测试时间期间，这些算法假定没有数据偏移，即$ \ pr_ \ text {train}（x，y）= \ pr_ \ pr_ \ text {test}}（x，y）$。可以为数据移动采用域自适应方法，但其中大多数旨在鼓励源数据和目标数据之间的类似特征分布。班级的有条件转移仍然会影响这种适应性。幸运的是，图可以在不同的数据分布中均匀地均匀地绘制图。作为响应，我们提出了移动射击节点分类（SRNC）来解决这些局限性。我们在目标图上引入了一个无监督的群集GNN，以通过同质图组对相似的节点进行分组。源图上使用标签信息的对抗损失用于聚类目标。然后，在训练图和目标图上的对抗样本上优化了一个移位式分类器，这些图形由群集GNN生成。我们对开放式偏移和表示转移进行实验，这证明了SRNC在推广到数据移位测试图上的出色精度。 SRNC始终比以前的SOTA域自适应算法要好，该算法逐渐使用目标图上的模型预测进行训练。
Graph Neural Networks (GNNs) are de facto node classification models in graph structured data. However, during testing-time, these algorithms assume no data shift, i.e., $\Pr_\text{train}(X,Y) = \Pr_\text{test}(X,Y)$. Domain adaption methods can be adopted for data shift, yet most of them are designed to only encourage similar feature distribution between source and target data. Conditional shift on classes can still affect such adaption. Fortunately, graph yields graph homophily across different data distributions. In response, we propose Shift-Robust Node Classification (SRNC) to address these limitations. We introduce an unsupervised cluster GNN on target graph to group the similar nodes by graph homophily. An adversarial loss with label information on source graph is used upon clustering objective. Then a shift-robust classifier is optimized on training graph and adversarial samples on target graph, which are generated by cluster GNN. We conduct experiments on both open-set shift and representation-shift, which demonstrates the superior accuracy of SRNC on generalizing to test graph with data shift. SRNC is consistently better than previous SoTA domain adaption algorithm on graph that progressively use model predictions on target graph for training.
