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Node2Vec is a state-of-the-art general-purpose feature learning method fornetwork analysis. However, current solutions cannot run Node2Vec on large-scalegraphs with billions of vertices and edges, which are common in real-worldapplications. The existing distributed Node2Vec on Spark incurs significantspace and time overhead. It runs out of memory even for mid-sized graphs withmillions of vertices. Moreover, it considers at most 30 edges for every vertexin generating random walks, causing poor result quality. In this paper, wepropose Fast-Node2Vec, a family of efficient Node2Vec random walk algorithms ona Pregel-like graph computation framework. Fast-Node2Vec computes transitionprobabilities during random walks to reduce memory space consumption andcomputation overhead for large-scale graphs. The Pregel-like scheme avoidsspace and time overhead of Spark's read-only RDD structures and shuffleoperations. Moreover, we propose a number of optimization techniques to furtherreduce the computation overhead for popular vertices with large degrees.Empirical evaluation show that Fast-Node2Vec is capable of computing Node2Vecon graphs with billions of vertices and edges on a mid-sized machine cluster.Compared to Spark-Node2Vec, Fast-Node2Vec achieves 7.7--122x speedups.

Efficient Graph Computation for Node2Vec