Search Results for author:
Found 2 papers, 0 papers with code
CDFGNN: a Systematic Design of Cache-based Distributed Full-Batch Graph Neural Network Training with Communication Reduction
Combined with communication quantization and hierarchical GP algorithm, CDFGNN outperforms the state-of-the-art distributed full-batch training frameworks by 30. 39% in our experiments.
An Efficient Pruning Process with Locality Aware Exploration and Dynamic Graph Editing for Subgraph Matching
Subgraph matching is a NP-complete problem that extracts isomorphic embeddings of a query graph $q$ in a data graph $G$.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
