School of Electronic Information, Wuhan University, Wuhan 430072, China.
Wuhan Digital Engineering Institute, Wuhan 430074, China.
Sensors (Basel). 2022 Nov 29;22(23):9306. doi: 10.3390/s22239306.
Hyperbolic embedding can effectively preserve the property of complex networks. Though some state-of-the-art hyperbolic node embedding approaches are proposed, most of them are still not well suited for the dynamic evolution process of temporal complex networks. The complexities of the adaptability and embedding update to the scale of complex networks with moderate variation are still challenging problems. To tackle the challenges, we propose hyperbolic embedding schemes for the temporal complex network within two dynamic evolution processes. First, we propose a low-complexity hyperbolic embedding scheme by using matrix perturbation, which is well-suitable for medium-scale complex networks with evolving temporal characteristics. Next, we construct the geometric initialization by merging nodes within the hyperbolic circular domain. To realize fast initialization for a large-scale network, an R tree is used to search the nodes to narrow down the search range. Our evaluations are implemented for both synthetic networks and realistic networks within different downstream applications. The results show that our hyperbolic embedding schemes have low complexity and are adaptable to networks with different scales for different downstream tasks.
双曲嵌入可以有效地保留复杂网络的特性。虽然已经提出了一些最先进的双曲节点嵌入方法，但它们大多数仍然不太适合时间复杂网络的动态演化过程。对于具有适度变化的复杂网络的适应性和嵌入更新的复杂性仍然是具有挑战性的问题。为了解决这些挑战，我们提出了两种动态演化过程中的时间复杂网络的双曲嵌入方案。首先，我们提出了一种基于矩阵摄动的低复杂度双曲嵌入方案，非常适合具有时变特性的中等规模复杂网络。接下来，我们通过合并双曲圆形域内的节点来构建几何初始化。为了实现大规模网络的快速初始化，使用 R 树搜索节点来缩小搜索范围。我们的评估在不同下游应用程序中的合成网络和真实网络上进行。结果表明，我们的双曲嵌入方案具有低复杂度，并且可以适应不同规模的网络用于不同的下游任务。
