Large real datasets as well as several synthetic datasets. Finally, we conducted extensive experiments on six Learning-based block loading model to leverage the advantages of the full-loadĪnd on-demand load methods. Second, to improve the I/O utilization, we design a
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Triangular bi-block scheduling strategy, the bucket-based walk management, and This paper introduces an I/O-efficient disk-based graph systemįor the scalable second-order random walk of large graphs, called GraSorw.įirst, to eliminate massive light vertex I/Os, we develop a bi-block executionĮngine that converts random I/Os into sequential I/Os by applying a new Models and suffer from expensive disk I/Os when executing the second-order
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Existingĭisk-based graph systems are only friendly to the first-order random walk Run second-order random walk-based applications on a single machine. Second-order random walk models and memory limitations, it is not scalable to in-memory backend typically performs faster than disk-based ones. Second-order PageRank) have become attractive. Alternatively, an in-memory JanusGraph graph can be opened directly in the Gremlin. Recently, second-order random walk-based applications (e.g., Node2vec, Unlike existing implementations which either use (full or partial) in-memory representations or rely on OS file system cache without guaranteeing real disk. The first-order random walk is poor at modeling higher-order structures in theĭata. However, as a simplification of real-world problems,
#Memory on disk graph pdf
Authors: Hongzheng Li, Yingxia Shao, Junping Du, Bin Cui, Lei Chen Download PDF Abstract: Random walk is widely used in many graph analysis tasks, especially theįirst-order random walk.