[R] Recurrent Distance-Encoding Neural Networks for Graph Representation Learning

**arXiv**: [https://arxiv.org/abs/2312.01538](https://arxiv.org/abs/2312.01538)

**OpenReview**: [https://openreview.net/forum?id=lNIj5FdXsC](https://openreview.net/forum?id=lNIj5FdXsC)

**Abstract**:

>Graph neural networks based on iterative one-hop message passing have been shown to struggle in harnessing information from distant nodes effectively. Conversely, graph transformers allow each node to attend to all other nodes directly, but suffer from high computational complexity and have to rely on ad-hoc positional encoding to bake in the graph inductive bias. In this paper, we propose a new architecture to reconcile these challenges. Our approach stems from the recent breakthroughs in …

abstract bias complexity computational encoding graph graph neural networks inductive information iterative machinelearning networks neural networks node paper positional encoding struggle transformers