arXiv (Cornell University)· 2017· Preprint
Graph Attention Networks
- 8,365citations
- 2017year
Short summary
Graph Attention Networks (GATs) introduce a novel neural network architecture for graph-structured data that uses masked self-attentional layers to assign different importance weights to neighboring nodes, overcoming limitations of graph convolutions without requiring costly matrix operations or prior knowledge of the graph structure.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces Graph Attention Networks (GATs) for graph-structured data.
- Employs masked self-attentional layers to weigh node importance within neighborhoods.
- Avoids costly matrix operations and upfront knowledge of graph structure.
- Achieves state-of-the-art results on transductive and inductive graph benchmarks.
AI-generated from the title and abstract; the full text is not read.
Abstract
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods' features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral-based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved or matched state-of-the-art results across four established transductive and inductive graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as a protein-protein interaction dataset (wherein test graphs remain unseen during training).
The authors' abstract, as published at the source. arXiv (Cornell University), 2017 · DOI ↗
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Field: Artificial Intelligence
Artificial IntelligenceComputer Science