CDI-Type II: Topology and Function in Computer, Social and Biological Networks

许多科学和工程领域的问题都集中在研究由具有不同属性和功能的相互联系的元素组成的大型复杂系统。这样的系统自然地被表示为网络，具有由网络元素之间的连接拓扑和元素本身固有的属性（或功能）分布组成的全局结构。网络表示的强大之处在于它能够在一个共同的形式框架内表达许多明显不同类型的系统，从而允许跨领域的计算和分析技术的交叉应用。虽然这种交叉适用性的前景是巨大的，但由于难以弥合不同研究领域之间的实质性鸿沟，进展一直受到阻碍。本研究的目标是利用计算机、社会和生物网络这三个领域的最新发展，实现一种综合的、跨学科的方法来研究复杂网络结构的系统的潜力。我们的研究重点是网络拓扑和属性之间的相互作用。我们将解决的核心问题包括：网络拓扑和元素属性或功能之间的交互建模；基于不完善和不完整数据的未知网络结构表征以及相关算法的发展，这些算法将有效地扩展到大型系统。计算效率是我们工作的一个重要方面，因为我们正在处理大量网络数据集的测量和分析。我们将使用我们开发的技术来解决三个应用领域的重要问题：计算机网络和安全（例如，检测互联网上恶意行为的方法）；在线社交网络（例如，在线环境中社会分层的再生产）；以及生物网络（例如疾病的生物特征）。结果将是一个统一的方法、软件工具和数据集的集合，这些方法、软件工具和数据集将使这些研究领域的发展成为可能并加速发展。这项工作的智力优势在于跨领域的富属性网络的网络拓扑和功能的联合分析。该项目将导致数据收集和分析的实用技术和方法的发展，这些技术和方法可应用于许多实质上不同的问题。成果的传播将通过研究出版物、可公开获得的软件和数据集以及与相关从业者社区的沟通来实现。该研究将与课程开发和学生咨询相结合，并将促进学生的跨学科培训。该项目将促进多样性，不仅通过研究团队的综合，还通过加强对在线环境中隔离和态度两极分化等现象的理解。

Problems in many science and engineering fields center on the study of large, complex systems consisting of interconnected elements with different attributes and function. Such systems are naturally represented as networks, with a global structure consisting of both the topology of connections among network elements and the distribution of attributes (or functions) inherent in the elements themselves. The power of the network representation lies in its ability to express many apparently different kinds of systems within a common formal framework, allowing for cross-application of computational and analytical techniques across fields. While the promise of such cross-applicability is great, advancement has been hindered by the difficulty of bridging the substantive gulf between different areas of research. The goal of this research is to leverage recent developments in three such areas, namely computer, social, and biological networks, to realize the potential of an integrated, interdisciplinary approach to the study of systems with complex network structure.Our research focuses specifically on the interaction between network topology and attributes. Central problems we will address include: the modeling of interactions between network topology and element attributes or function; the characterization of unknown network structure from imperfect and incomplete data; and the development of associated algorithms which will scale efficiently to large systems. Computational efficiency is an important dimension of our work, as we are dealing with the measurement and analysis of massive network data sets. We will use the techniques we develop to address important problems in three application domains: computer networks and security (e.g., methods for detection of malicious behavior on the Internet); online social networks (e.g., the reproduction of social stratification in online environments); and biological networks (e.g., biological signatures of disease). The result will be a unified collection of methods, software tools, and data sets that will enable and accelerate development in these research areas.The intellectual merit of this work lies in the joint analysis of network topology and function in attribute-rich networks across fields. The project will lead to the development of practical techniques and methodologies for data collection and analysis that can be applied to many substantively distinct problems. Dissemination of results will be achieved through research publications, publicly available software and data sets, and communication with relevant practitioner communities. The research will be integrated with curriculum development and student advising and will promote interdisciplinary training of students. The project will promote diversity, not only through the synthesis of the research team, but also through enhancing the understanding of phenomena such as segregation and attitude polarization in online environments.