EAGER: Efficient Methods for Characterizing Large-Scale Network Dynamics

许多现实世界的现象可以通过动态网络来建模，动态网络的连接性和活动性随着时间的推移而变化。因此，人们越来越有兴趣阐明这种网络的结构和动态。现有的方法主要集中在利用粗糙的网络属性或全局结构特征来理解网络动力学。这些方法通常依赖于静态网络的网络特征的扩展来理解动态网络，并且无法捕获真实世界网络的丰富动态。这个探索性的项目探索了一种层次化的方法来分解网络结构和动态，可以解释从节点级到社区级的多个尺度上的动态变化。这种方法是新颖的，而且由于其未经测试的性质，有点冒险。该研究围绕三个目标组织：（i）开发基于信息流的方法，可以通过同时优化复杂网络中的显式社区结构和部分流动态来提取多层动态。(ii)为动态感知网络总结开发一个计算框架，该框架保留了图的流动动态，并提供了大规模图动态的摘要。该项目推进了网络数据分析的当前最新技术。由此产生的工具，用于阐明复杂网络在多个尺度上的结构和动力学，可能会改变我们理解，设计，工程和控制复杂网络的方式。该项目丰富了韦恩州立大学以研究为基础的培训和推广活动。

Many real-world phenomena can be modeled by dynamic networks whose connectivity as well as activity changes over time. Hence, there is a growing interest in elucidating the structure and dynamics of such networks. Existing approaches to this problem focus mainly on utilizing either coarse network properties or global structural features to comprehend network dynamics. Such methods often rely on extensions of network features of static networks to understand dynamic networks and fail to capture the rich dynamics of real-world networks. This exploratory project explores a hierarchical approach to decomposition of network structure and dynamics that can explain changing dynamics at multiple scales ranging from node-level to community-level. The approach is novel, and because of its untested nature, somewhat risky. The research is organized around three aims:(i) Develop information-theoretic flow based approaches that can extract multiple layers of dynamics by simultaneously optimizing for explicit community structures and partial flow dynamics in complex networks. (ii) Develop a computational framework for dynamics-aware network summarization that preserves the flow dynamics of graphs and provides a summary of the large-scale graph dynamics.The project advances the current state-of-the-art in network data analytics. The resulting tools for elucidating the structure and dynamics of complex networks at multiple scales could potentially transform the way we understand, design, engineer, and control complex networks. The project enriches research-based training and outreach activities at Wayne State University.