Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph
This addresses memory efficiency for GNNs in industrial-scale graph applications, though it is incremental as it adapts NLP compression techniques to graphs.
The paper tackles the problem of training node embeddings for graph neural networks (GNNs) on large-scale graphs, where memory constraints make end-to-end training impractical, by proposing a compression method using bit vectors instead of floating-point vectors, achieving superior performance compared to alternatives.
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 parameters associated with the embeddings grows linearly with the number of nodes. It is therefore impractical to train the input node embeddings together with GNNs within graphics processing unit (GPU) memory in an end-to-end fashion when dealing with industrial-scale graph data. Inspired by the embedding compression methods developed for natural language processing (NLP) tasks, we develop a node embedding compression method where each node is compactly represented with a bit vector instead of a floating-point vector. The parameters utilized in the compression method can be trained together with GNNs. We show that the proposed node embedding compression method achieves superior performance compared to the alternatives.