CIF: Small: BCSP: Rethinking Network Structure: The Role of Interactions in the Analysis of Network Structure

社会、技术和生物系统通常被表示为一个复杂的网络，其结构揭示了系统的重要属性，并导致对其行为的见解。在过去的几十年里，人们提出了许多方法来分析复杂网络的结构，例如，在社会网络中识别有影响力的人，或在蛋白质-蛋白质相互作用网络中识别功能模块。这些方法中的大多数只检查实体之间的连接，而忽略它们之间的交互。然而，近年来，人们已经认识到网络结构是其连接（即拓扑）和网络中实体之间发生的相互作用的产物。这些相互作用决定了思想、信号、病原体或影响如何沿着连接流动，不同的相互作用可能导致对网络结构的不同看法。本研究项目将为了解网络结构、拓扑结构和动态相互作用之间的相互作用奠定基础。它将开发一个用于交互感知网络分析的数学框架，这将导致找到社区或模块的原则度量和算法，测量网络中实体之间的接近程度以及网络之间的距离。理论发现将在诸如识别模块、预测缺失链接等任务上使用现实世界的网络数据进行实证验证。这种网络分析的新框架将转化为计算机科学、社会学、生物学和医学的新发现。

Social, technological, and biological systems are often represented as a complex network, whose structure reveals important properties of the system and leads to insights about its behavior. Many methods have been proposed over the past several decades to analyze the structure of complex networks, for example, to identify influential people in a social network or functional modules in a protein-protein interaction network. Most of these methods examine connections between entities only, and ignore interactions between them. In recent years, however, it has been recognized that network structure is the product of both its connections (i.e., topology) and the interactions taking place between entities in the network. These interactions determine how ideas, signals, pathogens, or influence flow along the connections, and different interactions may lead to different views of network structure.This research project will lay the foundation for understanding the interplay between network structure, topology and dynamical interactions. It will develop a mathematical framework for interactions-aware network analysis that will lead to principled metrics and algorithms for finding communities or modules, measuring proximity between entities in a network, and distance between networks. Theoretical findings will be empirically validated using real-world network data on tasks such as identifying modules, predicting missing links and others. This new framework for network analysis will translate into new discoveries in computer science, sociology, biology and medicine.