As a fundamental problem in graph analysis, core decomposition aims to compute the core numbers of vertices in a given graph. It is a powerful tool for mining important graph structures. For dynamic graphs with real-time updates of vertices/edges, core maintenance has been utilized to update the core numbers of vertices. The previous approaches to core maintenance face challenges in terms of storage and efficiency. In this article, we investigate distributed approaches to core maintenance on a pregel-like system, which is a famous graph computing system. We first design a core decomposition algorithm to obtain core numbers of vertices in a given graph. Based on it, a distributed batch-stream combined algorithm (DBCA) is devised to efficiently maintain the core numbers when vertex/edge updates happen. In particular, we introduce a new task assignment strategy to DBCA based on diversity of the edge-cores of updated edges. To ensure that DBCA can accurately process core maintenance, we develop a message interaction protocol to resolve the problem of crosstalk among different tasks. Comprehensive experiments have been conducted on real/synthetic graphs, more specifically, in two typical distributed environments built on Supercomputing Center and Alibaba Cloud. The experiment results demonstrate that our proposed algorithms are efficient and scalable.

中文翻译：

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大型动态图核心维护的高效分布式方法


作为图分析中的一个基本问题，核心分解旨在计算给定图中顶点的核心数量。它是挖掘重要图结构的强大工具。对于顶点/边实时更新的动态图，利用核心维护来更新顶点的核心数。以前的堆芯维护方法面临存储和效率方面的挑战。在本文中，我们研究了类 pregel 系统（著名的图计算系统）上的分布式核心维护方法。我们首先设计一个核心分解算法来获取给定图中顶点的核心数量。在此基础上，设计了一种分布式批流组合算法（DBCA），以在点/边更新发生时有效地维护核心数。特别是，我们基于更新边缘的边缘核心的多样性，向 DBCA 引入了一种新的任务分配策略。为了保证DBCA能够准确地处理核心维护，我们开发了消息交互协议来解决不同任务之间的串扰问题。在真实/合成图上进行了全面的实验，更具体地说，在超算中心和阿里云构建的两个典型分布式环境中进行了实验。实验结果表明我们提出的算法是高效且可扩展的。