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

CIF：小：BCSP：重新思考网络结构：交互在网络结构分析中的作用

• 批准号: 1217605
• 负责人: Kristina Lerman
• 金额: $ 45万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217605&HistoricalAwards=false
• 关键词: CIF; Small; BCSP; Rethinking; Network

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.

社会、技术和生物系统通常被表示为复杂网络，其结构揭示了系统的重要属性，并导致对其行为的洞察。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，识别社会网络中有影响力的人或蛋白质相互作用网络中的功能模块。 大多数这些方法仅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）以及网络中实体之间发生的交互。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沿着流动，不同的相互作用可能导致对网络结构的不同看法，本研究项目将为理解网络结构、拓扑结构和动力学相互作用之间的相互作用奠定基础。它将为交互感知网络分析开发一个数学框架，这将导致原则性的度量和算法，用于寻找社区或模块，测量网络中实体之间的接近度，以及网络之间的距离。 理论研究结果将使用真实世界的网络数据进行经验验证，例如识别模块，预测缺失的链接等。这种新的网络分析框架将转化为计算机科学、社会学、生物学和医学领域的新发现。