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Ordered Subgraph Aggregation Networks

Chendi Qian, Gaurav Rattan, Floris Geerts, Christopher Morris, Mathias Niepert

Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS), pp. , 2022.


Abstract

Numerous subgraph-enhanced graph neural networks (GNNs) have emerged recently, provably boosting the expressive power of standard (message-passing) GNNs. However, there is a limited understanding of how these approaches relate to each other and to the Weisfeilerā€“Leman hierarchy. Moreover, current approaches either use all subgraphs of a given size, sample them uniformly at random, or use hand-crafted heuristics instead of learning to select subgraphs in a data-driven manner. Here, we offer a unified way to study such architectures by introducing a theoretical framework and extending the known expressivity results of subgraph-enhanced GNNs. Concretely, we show that increasing subgraph size always increases the expressive power and develop a better understanding of their limitations by relating them to the established k-š¯–¶š¯–« hierarchy. In addition, we explore different approaches for learning to sample subgraphs using recent methods for backpropagating through complex discrete probability distributions. Empirically, we study the predictive performance of different subgraph-enhanced GNNs, showing that our data-driven architectures increase prediction accuracy on standard benchmark datasets compared to non-data-driven subgraph-enhanced graph neural networks while reducing computation time.

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BibTeX

@inproceedings{qian22_neurips, title = {Ordered Subgraph Aggregation Networks}, author = {Qian, Chendi and Rattan, Gaurav and Geerts, Floris and Morris, Christopher and Niepert, Mathias}, year = {2022}, pages = {}, booktitle = {Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS)}, preprint = {https://arxiv.org/abs//2206.11168} }