A paper presented at ICLR 2018 describes Graph Attention Networks, which weight information from neighboring nodes using weights calculated for connected pairs. The record was published on February 12, 2026.
The paper presented at ICLR 2018 introduces Graph Attention Networks (GAT), an attention-based approach for aggregating graph data. A shared neural network calculates attention weights for pairs of connected nodes, allowing information received from neighbors to be weighted.
The bookmark suggests comparing this approach with other graph neural network architectures. To verify its scope and findings, consult the original ICLR 2018 paper and check its methodological and experimental details; this bookmark description provides no metrics or comparison results.