Efficient and Local Parallel Random Walks
This addresses a bottleneck in machine learning applications like clustering and semi-supervised learning by enabling local computation, though it is incremental over recent parallel methods.
The paper tackles the problem of computing random walks efficiently and locally from a subset of nodes in a graph, presenting a new algorithm that overcomes the non-locality limitation of prior work, with experimental results showing significantly improved scalability.
Random walks are a fundamental primitive used in many machine learning algorithms with several applications in clustering and semi-supervised learning. Despite their relevance, the first efficient parallel algorithm to compute random walks has been introduced very recently (Lacki et al.). Unfortunately their method has a fundamental shortcoming: their algorithm is non-local in that it heavily relies on computing random walks out of all nodes in the input graph, even though in many practical applications one is interested in computing random walks only from a small subset of nodes in the graph. In this paper, we present a new algorithm that overcomes this limitation by building random walk efficiently and locally at the same time. We show that our technique is both memory and round efficient, and in particular yields an efficient parallel local clustering algorithm. Finally, we complement our theoretical analysis with experimental results showing that our algorithm is significantly more scalable than previous approaches.