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CoreCube: Core Decomposition in Multilayer Graphs

机译:CoreCube:多层图中的核心分解

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Many real-life complex networks are modelled as multilayer graphs where each layer records a certain kind of interaction among entities. Despite the powerful modelling functionality, the decomposition on multilayer graphs remains unclear and inefficient. As a well-studied graph decomposition, core decomposition is efficient on a single layer graph with a variety of applications on social networks, biology, finance and so on. Nevertheless, core decomposition on multilayer graphs is much more challenging due to the various combinations of layers. In this paper, we propose efficient algorithms to compute the CoreCube which records the core decomposition on every combination of layers. We also devise a hybrid storage method that achieves a superior trade-off between the size of CoreCube and the query time. Extensive experiments on 8 real-life datasets demonstrate our algorithms are effective and efficient.
机译:许多现实生活中的复杂网络被建模为多层图,其中每一层记录实体之间的某种相互作用。尽管具有强大的建模功能,但多层图上的分解仍然不清楚且效率低下。作为深入研究的图分解,核心分解在单层图上非常有效,并且在社交网络,生物学,金融等方面具有多种应用。然而,由于层的各种组合,多层图上的核心分解更具挑战性。在本文中,我们提出了有效的算法来计算CoreCube,该算法记录了每个层组合上的核心分解。我们还设计了一种混合存储方法,该方法在CoreCube的大小和查询时间之间实现了出色的折衷。在8个真实数据集上的大量实验证明了我们的算法是有效且高效的。

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