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StreamFaSE: An Online Algorithm for Subgraph Counting in Dynamic Networks

机译:StreamFase:动态网络中子图计数的在线算法

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Counting subgraph occurrences in complex networks is an important analytical task with applicability in a multitude of domains such as sociology, biology and medicine. This task is a fundamental primitive for concepts such as motifs and graphlet degree distributions. However, there is a lack of online algorithms for computing and updating subgraph counts in dynamic networks. Some of these networks exist as a streaming of edge additions and deletions that are registered as they occur in the real world. In this paper we introduce StreamFaSE, an efficient online algorithm for keeping track of exact subgraph counts in dynamic networks, and we explain in detail our approach, showcasing its general applicability in different network scenarios. We tested our method on a set of diverse real directed and undirected network streams, showing that we are always faster than the current existing methods for this task, achieving several orders of magnitude speedup when compared to a state-of-art baseline.
机译:计算复杂网络中的子图出现是一个重要的分析任务,其在多种域中具有适用性,例如社会学,生物学和医学。此任务是基本原始的基本原语,例如图案和石墨度分布等概念。然而,缺乏用于计算和更新动态网络中的子图计数的在线算法。其中一些网络作为边缘添加和删除的流存在,它们在现实世界中出现时已注册。在本文中,我们介绍StreamFase,一种有效的在线算法,用于跟踪动态网络中的精确子图计数,我们详细解释了我们的方法,展示了其在不同网络方案中的一般适用性。我们在一组各种实际定向和无向网络流上测试了我们的方法,显示我们总是比目前此任务的现有方法更快,与最先进的基线相比,实现了几个数量级加速。

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