CAREER: Machine Learning Methods and Statistical Analysis Tools for Single Network Domains

职业生涯：单一网络域的机器学习方法和统计分析工具机器学习研究人员专注于结构化网络数据的两种截然不同的学习场景(即，链接节点的属性之间存在统计相关性)。在第一种情况下，领域由一组结构化示例(例如化合物)组成，随着结构化示例数量的增加，我们可以对学习算法进行渐近推理。在第二种情况下，域由单个潜在无限大的网络(例如，万维网)组成。在这些“单一网络”域中，数据的增加对应于获取底层网络的更大部分。即使存在一组可用于学习和预测的网络样本，它们也对应于从相同基础网络中提取的子网络，因此可能是相关的。虽然统计关系学习领域中的估计和推理方法已经成功地应用于单网络领域，但算法最初是针对网络总体而开发的，因此在单网络中进行学习和推理的理论基础很少。这项工作的重点是开发单一网络域的稳健统计方法--因为许多关于复杂系统的大型网络数据集很少有多于几个子网络可用于模型估计和评估。具体来说，该项目的目标包括：(1)加强单一网络领域学习的理论基础；(2)建立准确的方法来确定所发现的模式和特征的重要性；(3)建立新的模型选择和评估方法；(4)根据单一网络领域的独特特征，开发改进的网络学习和预测方法。将机器学习技术的适用性扩展到单个网络领域可能会在广泛的领域(例如，心理学、通信、教育、政治学)产生变革性的影响，在这些领域中，当前的方法将研究限制在二元或小群体环境中的过程的调查。此外，该项目的成果将作为计算机科学在更广泛的网络科学背景下的应用范例，这将吸引和留住那些本来可能不会对传统的计算机科学主题感兴趣的学生。有关更多详细信息，请访问see:http://www.cs.purdue.edu/homes/neville/research-nsf-career.html

CAREER: Machine Learning Methods and Statistical Analysis Tools for Single Network DomainsMachine learning researchers focus on two distinct learning scenarios for structured network data (i.e., where there are statistical dependencies among the attributes of linked nodes). In the first scenario, the domain consists of a population of structured examples (e.g., chemical compounds) and we can reason about learning algorithms asymptotically, as the number of structured examples increases. In the second scenario, the domain consists of a single, potentially infinite-sized network (e.g., the World Wide Web). In these "single network" domains, an increase in data corresponds to acquiring a larger portion of the underlying network. Even when there are a set of network samples available for learning and prediction, they correspond to subnetworks drawn from the same underlying network and thus may be dependent. Although estimation and inference methods from the field of statistical relational learning have been successfully applied in single-network domains, the algorithms were initially developed for populations of networks, and thus the theoretical foundation for learning and inference in single networks is scant. This work focuses on the development of robust statistical methods for single network domains -- since many large network datasets about complex systems rarely have more than a few subnetworks available for model estimation and evaluation. Specifically, the aims of the project include (1) strengthening the theoretical foundation for learning in single network domains, (2) creating accurate methods for determining the significance of discovered patterns and features, (3) formulating novel model selection and evaluation methods, and (4) developing improved approaches for network learning and prediction based on the unique characteristics of single network domains.The research will enhance our understanding of the mechanisms that influence the performance of network analysis methods and drive the development novel methods for complex network domains. Expanding the applicability of machine learning techniques for single network domains could have a transformational impact across a broad range of areas (e.g., psychology, communications, education, political science) where current methods limit research to the investigation of processes in dyad or small group settings. Also, the project results will serve as an example application of computer science in the broader network science context, which will attract and retain students that might not otherwise be engaged by conventional CS topics. For more details see:http://www.cs.purdue.edu/homes/neville/research-nsf-career.html