Fisher Information Embedding for Node and Graph Learning. (arXiv:2305.07580v1 [stat.ML])

Attention-based graph neural networks (GNNs), such as graph attention
networks (GATs), have become popular neural architectures for processing
graph-structured data and learning node embeddings. Despite their empirical
success, these models rely on labeled data and the theoretical properties of
these models have yet to be fully understood. In this work, we propose a novel
attention-based node embedding framework for graphs. Our framework builds upon
a hierarchical kernel for multisets of subgraphs around nodes (e.g.
neighborhoods) and each kernel leverages the …

architectures arxiv attention become data embedding embeddings fisher gnns graph graph learning graph neural networks information networks neural architectures neural networks node popular processing structured data success work