This paper addresses the challenge of change point detection in temporal networks, a critical task across various domains, including life sciences and socioeconomic activities. Continuous analysis and problem-solving within dynamic networks are essential in these fields. While much attention has been given to binary cases, this study extends the scope to include change point detection in weighted networks, an important dimension of edge analysis in dynamic networks. We introduce a novel distance metric called the Interval Sum Absolute Difference Distance (ISADD) to measure the distance between two graph snapshots. Additionally, we apply a Gaussian radial basis function to transform this distance into a similarity score between graph snapshots. This similarity score function effectively identifies individual change points. Furthermore, we employ a bisection detection algorithm to extend the method to detect multiple change points. Experimental results on both simulated and real-world data demonstrate the efficacy of the proposed framework.

中文翻译：

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基于图快照相似性度量的时态网络中的变化点检测


本文解决了时间网络中变化点检测的挑战，这是一项跨各个领域的关键任务，包括生命科学和社会经济活动。在这些领域中，动态网络中的持续分析和问题解决至关重要。虽然对二元情况给予了很大关注，但本研究将范围扩展到包括加权网络中的变化点检测，这是动态网络中边缘分析的一个重要维度。我们引入了一种称为间隔和绝对差值距离 （ISADD） 的新型距离指标，用于测量两个图形快照之间的距离。此外，我们应用高斯径向基函数将此距离转换为图形快照之间的相似性分数。此相似性评分函数可有效识别单个变化点。此外，我们采用二分检测算法来扩展该方法以检测多个变化点。模拟和真实世界数据的实验结果证明了所提出的框架的有效性。