Fast Iterative Graph Computing with Updated Neighbor States
Yijie Zhou, Shufeng Gong, Feng Yao, Hanzhang Chen, Song Yu, Pengxi, Liu, Yanfeng Zhang, Ge Yu, Jeffrey Xu Yu

TL;DR
This paper introduces GoGraph, a graph reordering method that optimizes vertex processing order to reduce iteration rounds and accelerate iterative graph computations, demonstrating significant performance improvements.
Contribution
The paper proposes a novel reordering algorithm, GoGraph, which effectively reduces iteration rounds and accelerates graph processing by optimizing vertex processing order.
Findings
GoGraph outperforms existing algorithms by up to 3.34x in runtime.
The vertex processing order correlates with the number of iterative rounds.
A metric function effectively quantifies processing order efficiency.
Abstract
Enhancing the efficiency of iterative computation on graphs has garnered considerable attention in both industry and academia. Nonetheless, the majority of efforts focus on expediting iterative computation by minimizing the running time per iteration step, ignoring the optimization of the number of iteration rounds, which is a crucial aspect of iterative computation. We experimentally verified the correlation between the vertex processing order and the number of iterative rounds, thus making it possible to reduce the number of execution rounds for iterative computation. In this paper, we propose a graph reordering method, GoGraph, which can construct a well-formed vertex processing order effectively reducing the number of iteration rounds and, consequently, accelerating iterative computation. Before delving into GoGraph, a metric function is introduced to quantify the efficiency of…
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Taxonomy
TopicsGraph Theory and Algorithms · Advanced Graph Neural Networks · Data Management and Algorithms
