基于张量分解的动态网络时间聚类
Temporal Clustering in Dynamic Networks with Tensor Decomposition
摘要：
动态网络越来越多地被用于模拟真实世界的数据集。在他们的分析具有挑战性的任务是检测和表征集群。它对于分析真实世界的数据非常有用，例如检测网络中不断发展的社区。我们提出了一个基于一组网络生成模型的时间聚类框架来解决这个问题。我们使用PARAFAC分解从数据集中学习网络模型，然后使用$K$ -means进行聚类，使用Silhouette准则确定聚类的数量，并使用相似性得分对聚类进行排序并保留重要的聚类。为了解决这些集群的时间依赖性方面，我们提出了一种分割算法来检测它们的形成，溶解和寿命。使用具有地面真实数据集和真实世界数据集的合成网络来测试我们的方法，结果表明我们的方法在聚类和生命周期检测方面比以前的方法具有更好的性能。
英文摘要：
Dynamic networks are increasingly being usedd to model real world datasets. A challenging task in their analysis is to detect and characterize clusters. It is useful for analyzing real-world data such as detecting evolving communities in networks. We propose a temporal clustering framework based on a set of network generative models to address this problem. We use PARAFAC decomposition to learn network models from datasets.We then use $K$-means for clustering, the Silhouette criterion to determine the number of clusters, and a similarity score to order the clusters and retain the significant ones. In order to address the time-dependent aspect of these clusters, we propose a segmentation algorithm to detect their formations, dissolutions and lifetimes. Synthetic networks with ground truth and real-world datasets are used to test our method against state-of-the-art, and the results show that our method has better performance in clustering and lifetime detection than previous methods.
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