  Graph is a ubiquitous representation of data in various research fields, and
graph embedding is a prevalent machine learning technique for capturing key
features and generating fixed-sized attributes. However, most state-of-the-art
graph embedding methods are computationally and spatially expensive. Recently,
the Graph Encoder Embedding (GEE) has been shown as the fastest graph embedding
technique and is suitable for a variety of network data applications. As
real-world data often involves large and sparse graphs, the huge sparsity
usually results in redundant computations and storage. To address this issue,
we propose an improved version of GEE, sparse GEE, which optimizes the
calculation and storage of zero entries in sparse matrices to enhance the
running time further. Our experiments demonstrate that the sparse version
achieves significant speedup compared to the original GEE with Python
implementation for large sparse graphs, and sparse GEE is capable of processing
millions of edges within minutes on a standard laptop.
