论文标题
Instantembedding：有效的本地节点表示
InstantEmbedding: Efficient Local Node Representations
论文作者
论文摘要
在本文中，我们介绍了Instantembedding，这是一种使用局部Pagerank计算生成单个节点表示的有效方法。从理论上讲，我们证明我们的方法在均匀时期产生全球一致的表示。我们通过对数十亿多个边缘进行现实世界数据集进行广泛的实验来证明这一点。我们的实验证实，与传统方法相比，即将构成的计算时间（超过9,000倍）和记忆（超过8,000次）的记忆比传统方法（包括DeepWalk，Node2VEC，Verse和Fastrp）产生单个节点的嵌入。我们还表明，我们的方法会产生高质量的表示形式，证明了在节点分类和链接预测等任务的无监督表示学习中符合或超过艺术状态的结果。
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.
