Search Results for author:
Found 2 papers, 0 papers with code
Deep Graph Neural Networks via Flexible Subgraph Aggregation
Based on this, we propose a sampling-based node-level residual module (SNR) that can achieve a more flexible utilization of different hops of subgraph aggregation by introducing node-level parameters sampled from a learnable distribution.
Adaptive Depth Graph Attention Networks
As one of the most popular GNN architectures, the graph attention networks (GAT) is considered the most advanced learning architecture for graph representation and has been widely used in various graph mining tasks with impressive results.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
