基于随机游动的多层时间网络多维HITS

Laishui Lv, Kun Zhang
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引用次数: 0

摘要

为了识别静态网络中的重要节点,已经建立了许多中心性度量,其中HITS中心性作为一种排序方法被广泛使用。在本文中,我们将经典的HITS中心性扩展到具有有向边的多层时态网络中的节点排序。首先,我们使用一个六阶张量来表示多层时间网络,然后通过构造六个转移概率张量在建立的六阶张量中引入随机游动。其次,我们基于这些构造的张量建立张量方程,以获得六个中心性向量:两个用于节点,两个用于层和两个用于时间戳。此外,我们还在一定条件下证明了所提出的中心性测度的存在性。最后,我们通过实验证明了所提出的中心性在合成网络和现实世界网络上的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Dimensional HITS Based on Random Walks for Multilayer Temporal Networks
Numerous centrality measures have been established to identify the important nodes in static networks, among them, HITS centrality is widely used as a ranking method. In this paper, we extend the classical HITS centrality to rank nodes in multilayer temporal networks with directed edges. First, we use a sixth-order tensor to represent multilayer temporal network and then introduce random walks in the established sixth-order tensor by constructing six transition probability tensors. Second, we establish tensor equations based on these constructed tensors to obtain six centrality vectors: two for the nodes, two for the layers and two for the time stamps. Besides, we prove the existence of the proposed centrality measure under some conditions. Finally, we experimentally show the effectiveness of the proposed centrality on an synthetic network and a real-world network.
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