Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph. (arXiv:2208.05648v1 [cs.LG])

Graph neural networks (GNNs) are deep learning models designed specifically
for graph data, and they typically rely on node features as the input to the
first layer. When applying such a type of network on the graph without node
features, one can extract simple graph-based node features (e.g., number of
degrees) or learn the input node representations (i.e., embeddings) when
training the network. While the latter approach, which trains node embeddings,
more likely leads to better performance, the number of …

arxiv compression embedding graph hashing learning lg representation representation learning scale