Neural Priority Queues for Graph Neural Networks (GNNs)
Keywords: Neural PQs, Differentiable Priority Queues, GNN, Memory, Algorithmic Reasoning, Representational Learning
TL;DR: We propose Neural PQs, a differentiable analouge to priority queues, and show their prowess with algorithmic reasoning tasks.
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.
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 2536
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