在动态网络中统一局部和全局变化检测
Unifying Local and Global Change Detection in Dynamic Networks
摘要：
许多现实世界的网络是复杂的动态系统，其中既有局部（例如，改变节点属性）和全局（例如，改变网络拓扑）过程随时间展开。局部动态可能会引发网络的全局变化，检测这种影响的能力可能对许多现实问题产生深远的影响。大多数现有的技术分别侧重于局部或全球方面的问题，或处理这两个彼此隔离。在本文中，我们提出了一种新的网络模型，同时占本地和全球的动态。据我们所知，这是第一次尝试通过统一的生成框架来建模和检测动态网络上的局部和全局变化点。我们的模型是建立在流行的混合成员随机块模型（MMSB）与稀疏的共同发展模式。我们推导出一个有效的随机梯度朗之万动力学（SGLD）采样器，我们提出的模型，这使得它能够扩展到潜在的非常大的网络。最后，我们验证了我们的模型的合成和真实世界的数据，并证明了它的优越性，在几个基线。
英文摘要：
Many real-world networks are complex dynamical systems, where both local (e.g., changing node attributes) and global (e.g., changing network topology) processes unfold over time. Local dynamics may provoke global changes in the network, and the ability to detect such effects could have profound implications for a number of real-world problems. Most existing techniques focus individually on either local or global aspects of the problem or treat the two in isolation from each other. In this paper we propose a novel network model that simultaneously accounts for both local and global dynamics. To the best of our knowledge, this is the first attempt at modeling and detecting local and global change points on dynamic networks via a unified generative framework. Our model is built upon the popular mixed membership stochastic blockmodels (MMSB) with sparse co-evolving patterns. We derive an efficient stochastic gradient Langevin dynamics (SGLD) sampler for our proposed model, which allows it to scale to potentially very large networks. Finally, we validate our model on both synthetic and real-world data and demonstrate its superiority over several baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
