在连续时间的时间网络中频繁的模式采矿
IEEE transactions on pattern analysis and machine intelligence
|October 16, 2023
概括
这项研究引入了一种分析时间网络的新方法,以无损保存网络动态. 这种方法可以在复杂的网络数据中更有效地进行频繁的时间模式挖掘.
科学领域:
- 网络科学 网络科学
- 数据挖掘 数据挖掘
- 计算机科学 计算机科学
背景情况:
- 时间网络在各种学科中至关重要.
- 频繁的模式挖掘对于网络分析至关重要.
- 现有的方法经常将时间网络表示为静态序列,导致计算-表达性权衡.
研究的目的:
- 为时间网络提出一种新的,无损的表示.
- 引入受约束的间隔图 (CIG).
- 开发用于挖掘频繁时间模式的算法.
主要方法:
- 开发了一种新的网络表示,可以无损地保存时间方面.
- 引入并使用受约束间隔图 (CIG).
- 设计了用于挖掘常见时间模式的完整集的算法,考虑了四个等态定义.
主要成果:
- 拟议的代表性有效地捕捉了网络的时间动态.
- 算法成功地从现实世界的数据集中挖掘出频繁的时间模式.
- 证明了该方法的实用性和模式发现能力.
结论:
- 新的表示和算法为时间网络分析提供了显著的进步.
- 该方法克服了静态网络表示的局限性.
- 这种方法对于在各种时间网络设置中发现未知的模式是实用的和有效的.
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