In this paper, we introduce InstantEmbedding, an efficient method for
generating single-node representations using local PageRank computations. We
theoretically prove that our approach produces globally consistent
representations in sublinear time. We demonstrate this empirically by
conducting extensive experiments on real-world datasets with over a billion
edges. Our experiments confirm that InstantEmbedding requires drastically less
computation time (over 9,000 times faster) and less memory (by over 8,000
times) to produce a single node's embedding than traditional methods including
DeepWalk, node2vec, VERSE, and FastRP. We also show that our method produces
high quality representations, demonstrating results that meet or exceed the
state of the art for unsupervised representation learning on tasks like node
classification and link prediction.

本文介绍了一种高效的用于生成单节点表示的方法 InstantEmbedding，并进行了理论证明和大规模实验，结果表明其比传统方法需要的计算时间少 9000 倍以上，同时其所生成的表示具有很高的质量。