论文标题
在动态网络中的潜在空间方法可以进行社区检测
Latent Space Approaches to Community Detection in Dynamic Networks
论文作者
论文摘要
长期以来，将二元数据嵌入潜在空间一直是对各种网络建模网络的流行方法。尽管使用此方法用于静态网络，但本文在动态网络数据中提供了两种社区检测方法，这些方法基于文献中先前提出的距离和投影模型。我们提出的方法捕获了数据的随时间变化的方面，可以对有向或无方向的边缘进行建模，从而固有地纳入了传递性并说明每个演员形成边缘的个人倾向。我们提供贝叶斯估计算法，并将这些方法应用于排名的动态友谊网络和世界出口/进口数据。
Embedding dyadic data into a latent space has long been a popular approach to modeling networks of all kinds. While clustering has been done using this approach for static networks, this paper gives two methods of community detection within dynamic network data, building upon the distance and projection models previously proposed in the literature. Our proposed approaches capture the time-varying aspect of the data, can model directed or undirected edges, inherently incorporate transitivity and account for each actor's individual propensity to form edges. We provide Bayesian estimation algorithms, and apply these methods to a ranked dynamic friendship network and world export/import data.
