Bayesian Link Prediction with Deep Graph Convolutional Gaussian Processes

Felix Opolka, Pietro Lió
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, PMLR 151:4835-4852, 2022.

Abstract

Link prediction aims to reveal missing edges in a graph. We introduce a deep graph convolutional Gaussian process model for this task, which addresses recent challenges in graph machine learning with oversmoothing and overfitting. Using simplified graph convolutions, we transform a Gaussian process to leverage the topological information of the graph domain. To scale the Gaussian process model to larger graphs, we introduce a variational inducing point method that places pseudo-inputs on a graph-structured domain. Multiple Gaussian processes are assembled into a hierarchy whose structure allows skipping convolutions and thus counteracting oversmoothing. The proposed model represents the first Gaussian process for link prediction that makes use of both node features and topological information. We evaluate our model on multiple graph data sets with up to thousands of nodes and report consistent improvements over competitive link prediction approaches.

Cite this Paper

BibTeX
@InProceedings{pmlr-v151-opolka22b, title = { Bayesian Link Prediction with Deep Graph Convolutional Gaussian Processes }, author = {Opolka, Felix and Li\'o, Pietro}, booktitle = {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics}, pages = {4835--4852}, year = {2022}, editor = {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel}, volume = {151}, series = {Proceedings of Machine Learning Research}, month = {28--30 Mar}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v151/opolka22b/opolka22b.pdf}, url = {https://proceedings.mlr.press/v151/opolka22b.html}, abstract = { Link prediction aims to reveal missing edges in a graph. We introduce a deep graph convolutional Gaussian process model for this task, which addresses recent challenges in graph machine learning with oversmoothing and overfitting. Using simplified graph convolutions, we transform a Gaussian process to leverage the topological information of the graph domain. To scale the Gaussian process model to larger graphs, we introduce a variational inducing point method that places pseudo-inputs on a graph-structured domain. Multiple Gaussian processes are assembled into a hierarchy whose structure allows skipping convolutions and thus counteracting oversmoothing. The proposed model represents the first Gaussian process for link prediction that makes use of both node features and topological information. We evaluate our model on multiple graph data sets with up to thousands of nodes and report consistent improvements over competitive link prediction approaches. } }
Endnote
%0 Conference Paper %T Bayesian Link Prediction with Deep Graph Convolutional Gaussian Processes %A Felix Opolka %A Pietro Lió %B Proceedings of The 25th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2022 %E Gustau Camps-Valls %E Francisco J. R. Ruiz %E Isabel Valera %F pmlr-v151-opolka22b %I PMLR %P 4835--4852 %U https://proceedings.mlr.press/v151/opolka22b.html %V 151 %X Link prediction aims to reveal missing edges in a graph. We introduce a deep graph convolutional Gaussian process model for this task, which addresses recent challenges in graph machine learning with oversmoothing and overfitting. Using simplified graph convolutions, we transform a Gaussian process to leverage the topological information of the graph domain. To scale the Gaussian process model to larger graphs, we introduce a variational inducing point method that places pseudo-inputs on a graph-structured domain. Multiple Gaussian processes are assembled into a hierarchy whose structure allows skipping convolutions and thus counteracting oversmoothing. The proposed model represents the first Gaussian process for link prediction that makes use of both node features and topological information. We evaluate our model on multiple graph data sets with up to thousands of nodes and report consistent improvements over competitive link prediction approaches.
APA
Opolka, F. & Lió, P.. (2022). Bayesian Link Prediction with Deep Graph Convolutional Gaussian Processes . Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 151:4835-4852 Available from https://proceedings.mlr.press/v151/opolka22b.html.

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