Extensive experiments on various GNN models and large graph datasets show that BGL significantly outperforms existing GNN training systems by 20. 68x on average.
The advent of mixed-numerology multi-carrier (MN-MC) techniques adds flexibilities in supporting heterogeneous services in fifth generation (5G) communication systems and beyond.
Graph neural networks (GNNs) have been widely used for representation learning on graph data.
Representation learning on graphs, also called graph embedding, has demonstrated its significant impact on a series of machine learning applications such as classification, prediction and recommendation.
Neyshabur and Srebro proposed Simple-LSH, which is the state-of-the-art hashing method for maximum inner product search (MIPS) with performance guarantee.