论文标题
链接预测的图形卷积高斯流程
Graph Convolutional Gaussian Processes For Link Prediction
论文作者
论文摘要
链接预测旨在揭示图中缺少边缘。我们通过使用简化的图形卷积进行转换，以更好地利用域的电感偏置来解决此任务。为了将高斯过程模型扩展到大图，我们引入了一种诱导点方法，该方法将伪输入放在图形结构域上。我们在八个大图上评估了我们的模型，与现有的高斯过程模型以及与最新的图形神经网络方法相比，对现有高斯流程模型以及竞争性能的一致改进进行了评估。
Link prediction aims to reveal missing edges in a graph. We address this task with a Gaussian process that is transformed using simplified graph convolutions to better leverage the inductive bias of the domain. To scale the Gaussian process model to large graphs, we introduce a variational inducing point method that places pseudo inputs on a graph-structured domain. We evaluate our model on eight large graphs with up to thousands of nodes and report consistent improvements over existing Gaussian process models as well as competitive performance when compared to state-of-the-art graph neural network approaches.
