Neural Priority Queues for Graph Neural Networks

Rishabh Jain; Petar Veličković; Pietro Liò

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arXiv · 2307.09660

Neural Priority Queues for Graph Neural Networks

Rishabh Jain·Petar Veličković·Pietro Liò

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Abstract

Graph Neural Networks (GNNs) have shown considerable success in neural algorithmic reasoning. Many traditional algorithms make use of an explicit memory in the form of a data structure. However, there has been limited exploration on augmenting GNNs with external memory. In this paper, we present Neural Priority Queues, a differentiable analogue to algorithmic priority queues, for GNNs. We propose and motivate a desiderata for memory modules, and show that Neural PQs exhibit the desiderata, and reason about their use with algorithmic reasoning. This is further demonstrated by empirical results on the CLRS-30 dataset. Furthermore, we find the Neural PQs useful in capturing long-range interactions, as empirically shown on a dataset from the Long-Range Graph Benchmark.

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BibTeX RIS

Rishabh Jain, Petar Veličković, Pietro Liò. 2023-07-18. Neural Priority Queues for Graph Neural Networks. https://arxiv.org/abs/2307.09660

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