Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks. (arXiv:2203.00199v2 [cs.LG] UPDATED)

Graph neural networks (GNN) have shown great advantages in many graph-based
learning tasks but often fail to predict accurately for a task-based on sets of
nodes such as link/motif prediction and so on. Many works have recently
proposed to address this problem by using random node features or node distance
features. However, they suffer from either slow convergence, inaccurate
prediction, or high complexity. In this work, we revisit GNNs that allow using
positional features of nodes given by positional encoding …

arxiv graph graph neural networks networks neural networks positional encoding