# Efficient Graph Computation for Node2Vec

arXiv: https://arxiv.org/abs/1805.00280 · HTML: https://mlsanity.com/paper/1805.00280/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2018.

## Built on by (most cited first, 1 of 1)

- [NOMAD: Generating Embeddings for Massive Distributed Graphs](https://mlsanity.com/paper/2604.09419/) (2026)

## Abstract (arXiv)

Node2Vec is a state-of-the-art general-purpose feature learning method for network analysis. However, current solutions cannot run Node2Vec on large-scale graphs with billions of vertices and edges, which are common in real-world applications. The existing distributed Node2Vec on Spark incurs significant space and time overhead. It runs out of memory even for mid-sized graphs with millions of vertices. Moreover, it considers at most 30 edges for every vertex in generating random walks, causing poor result quality. In this paper, we propose Fast-Node2Vec, a family of efficient Node2Vec random walk algorithms on a Pregel-like graph computation framework. Fast-Node2Vec computes transition probabilities during random walks to reduce memory space consumption and computation overhead for large-scale graphs. The Pregel-like scheme avoids space and time overhead of Spark's read-only RDD structures and shuffle operations. Moreover, we propose a number of optimization techniques to further reduce the computation overhead for popular vertices with large degrees. Empirical evaluation show that Fast-Node2Vec is capable of computing Node2Vec on graphs with billions of vertices and edges on a mid-sized machine cluster. Compared to Spark-Node2Vec, Fast-Node2Vec achieves 7.7--122x speedups.