Graph Convolutional Gaussian Processes For Link Prediction
This work addresses link prediction for graph data, offering a scalable and improved Gaussian process approach, though it appears incremental as it builds on existing methods with specific enhancements.
The paper tackles link prediction in graphs by introducing a Gaussian process model enhanced with simplified graph convolutions to leverage domain inductive bias, and reports consistent improvements over existing Gaussian process models and competitive performance against state-of-the-art graph neural networks on eight large graphs with up to thousands of nodes.
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.