论文标题
贝叶斯非参数潜在空间方法，用于建模动态网络中不断发展的社区
A Bayesian Nonparametric Latent Space Approach to Modeling Evolving Communities in Dynamic Networks
论文作者
论文摘要
在动态（时变）网络数据中社区的演变是一个重要的主题。理解这些动态网络的一种流行方法是将二元关系嵌入到潜在的度量空间中。尽管存在用于动态网络的方法的聚类方法，但它们都假定静态社区结构。本文提出了一种用于动态网络的贝叶斯非参数模型，该模型可以建模具有不断发展的社区结构的网络。我们的模型通过明确建模具有分层dirichlet过程隐藏的马尔可夫模型来扩展现有的潜在空间方法。我们提出的方法，即层次的迪里奇过程潜在位置聚类模型（HDP-LPCM），结合了数据的传递性，模拟了数据的个体和组级别方面，并避免了大多数流行方法所需的组数量的计算昂贵选择。我们提供了马尔可夫链蒙特卡洛估计算法，并将我们的方法应用于合成和现实世界网络以证明其性能。
The evolution of communities in dynamic (time-varying) network data is a prominent topic of interest. A popular approach to understanding these dynamic networks is to embed the dyadic relations into a latent metric space. While methods for clustering with this approach exist for dynamic networks, they all assume a static community structure. This paper presents a Bayesian nonparametric model for dynamic networks that can model networks with evolving community structures. Our model extends existing latent space approaches by explicitly modeling the additions, deletions, splits, and mergers of groups with a hierarchical Dirichlet process hidden Markov model. Our proposed approach, the hierarchical Dirichlet process latent position clustering model (HDP-LPCM), incorporates transitivity, models both individual and group level aspects of the data, and avoids the computationally expensive selection of the number of groups required by most popular methods. We provide a Markov chain Monte Carlo estimation algorithm and apply our method to synthetic and real-world networks to demonstrate its performance.
