Jan 18, 2018 · The main idea is to avoid frequent communication over expensive network links using an adaptive edge migration strategy. Our evaluations show an ...
With this method, our algorithm ensures a clustered traffic behavior: partitions in the same partition cluster share the same replicas and thus are expected to ...
Abstract—Distributed graph processing systems such as Pregel, PowerGraph, or GraphX gained popularity due to their superior performance of data analytics on ...
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This repository contains information about graph processing. - graph-processing/README.md at master · dnasc/graph-processing. ... GrapH: Traffic-Aware Graph ...
To this end, we developed Grapes, the first graph processing system using vertex-cut graph partitioning that considers both, diverse vertex traffic and ...
First, a novel direction- and distance-aware self-attention module leverages relative position embedding to capture the direction and relative distance within ...
Gradoop: Analyzing Temporal Graphs with Gradoop; GrapH: Traffic-Aware Graph Processing; Graph3S: A Simple, Speedy and Scalable Distributed Graph Processing ...
In this paper, we present a novel out-of-core graph processing system called GraphSD, which optimizes the I/O traffic by simultaneously capturing the state and ...
Feb 20, 2024 · A scalable synthetic traffic model of Graph500 for computer networks analysis. Concurrency and Computation: Practice and Experience 29, 24 ...
Aug 3, 2024 · et al. Adapgl: An adaptive graph learning algorithm for traffic prediction based on spatiotemporal neural networks. Transp. Res. C. (2022). Luo ...