[Paper Review] Graph Neural Networks with Adaptive Readouts

David Buterez, Jon Paul Janet+3 more|arXiv (Cornell University)|Nov 9, 2022

Machine Learning in Materials Science25 citations

TL;DR

The paper investigates adaptive, neural readouts for graph neural networks to relax permutation invariance, showing improved performance across 40+ datasets and various convolution operators.

ABSTRACT

An effective aggregation of node features into a graph-level representation via readout functions is an essential step in numerous learning tasks involving graph neural networks. Typically, readouts are simple and non-adaptive functions designed such that the resulting hypothesis space is permutation invariant. Prior work on deep sets indicates that such readouts might require complex node embeddings that can be difficult to learn via standard neighborhood aggregation schemes. Motivated by this, we investigate the potential of adaptive readouts given by neural networks that do not necessarily give rise to permutation invariant hypothesis spaces. We argue that in some problems such as binding affinity prediction where molecules are typically presented in a canonical form it might be possible to relax the constraints on permutation invariance of the hypothesis space and learn a more effective model of the affinity by employing an adaptive readout function. Our empirical results demonstrate the effectiveness of neural readouts on more than 40 datasets spanning different domains and graph characteristics. Moreover, we observe a consistent improvement over standard readouts (i.e., sum, max, and mean) relative to the number of neighborhood aggregation iterations and different convolutional operators.

Motivation & Objective

• Motivate and assess whether adaptive (neural) readouts can provide benefits over standard, permutation-invariant readouts in GNNs.
• Examine how relaxing permutation invariance affects learning and model capacity in graph tasks.
• Evaluate performance gains across diverse datasets and graph characteristics.
• Investigate the impact of adaptive readouts relative to the number of neighborhood aggregation iterations and convolutional operators.

Proposed method

• Propose neural readouts that adaptively aggregate node features into a graph-level representation.
• Contrast adaptive readouts with traditional permutation-invariant readouts (sum, max, mean).
• Evaluate across multiple GNN architectures and a wide set of datasets.
• Analyze performance as a function of neighborhood aggregation depth and convolutional operators.
• Use empirical experiments to determine when adaptive readouts provide benefits.

Experimental results

Research questions

• RQ1 Do adaptive neural readouts improve graph-level representations compared to conventional readouts under permutation invariance constraints?
• RQ2 In which settings (datasets, graph characteristics) do adaptive readouts yield the largest gains?
• RQ3 How does the performance of adaptive readouts vary with the number of neighborhood aggregation iterations and the choice of convolution operator?
• RQ4 Can adaptive readouts enable better learning when molecules or graphs have canonical representations?
• RQ5 What is the generalization behavior of adaptive readouts across tasks beyond standard benchmarks?

Key findings

• Adaptive neural readouts yield improvements over standard readouts (sum, max, mean) across more than 40 datasets.
• Improvements are consistent across different numbers of aggregation iterations and various convolutional operators.
• Benefits appear in problems where canonical representations are common, suggesting relaxed permutation constraints can help learning.
• Neural readouts demonstrate effective model capacity without requiring strictly permutation-invariant hypothesis spaces.
• Empirical results cover diverse graph domains, underscoring broad applicability of adaptive readouts.