Dynamic networks represent the complex and evolving interrelationships between real-world entities. Given the scale and variability of these networks, finding an optimal slicing interval is essential for meaningful analysis. Nonuniform timeslicing, which adapts to density changes within the network, is drawing attention as a solution to this problem. In this research, we categorized existing algorithms into two domains -- data mining and visualization -- according to their approach to the problem. Data mining approach focuses on capturing temporal patterns of dynamic networks, while visualization approach emphasizes lessening the burden of analysis. We then introduce a novel nonuniform timeslicing method that synthesizes the strengths of both approaches, demonstrating its efficacy with a real-world data. The findings suggest that combining the two approaches offers the potential for more effective network analysis.

翻译：动态网络表征真实世界实体之间复杂且不断演变的相互关系。鉴于这些网络的规模和可变性，确定最优切片间隔对于有意义的分析至关重要。非均匀时间切片能够适应网络内的密度变化，作为解决该问题的一种方法正日益受到关注。在本研究中，我们根据现有算法处理该问题的方式，将其分为两个领域——数据挖掘和可视化。数据挖掘方法侧重于捕捉动态网络的时间模式，而可视化方法则强调减轻分析负担。随后，我们提出了一种新颖的非均匀时间切片方法，该方法综合了两种方法的优势，并通过真实世界数据证明了其有效性。研究结果表明，将这两种方法相结合有望实现更有效的网络分析。