k核分解的并行批动态算法及相关图问题

Proceedings of the 34th ACM Symposium on Parallelism in Algorithms and Architectures Pub Date : 2022-07-11 DOI:10.1145/3490148.3538569

Quanquan C. Liu, Jessica Shi, Shangdi Yu, Laxman Dhulipala, Julian Shun

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引用次数: 2

摘要

在动态图中快速维护k核分解在网络分析中有着重要的应用。设计高效精确算法的主要挑战是，对图的一次更新可能导致重大的全局更改。我们的论文关注的是具有小近似因子的近似算法，这些近似算法比精确算法更有效。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Parallel Batch-Dynamic Algorithms for k-Core Decomposition and Related Graph Problems

Maintaining a k-core decomposition quickly in a dynamic graph has important applications in network analysis. The main challenge for designing efficient exact algorithms is that a single update to the graph can cause significant global changes. Our paper focuses on approximation algorithms with small approximation factors that are much more efficient than what exact algorithms can obtain.

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Proceedings of the 34th ACM Symposium on Parallelism in Algorithms and Architectures

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