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

职业：单一网络域的机器学习方法和统计分析工具

• 批准号: 1149789
• 负责人: Jennifer Neville
• 金额: $ 49.66万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1149789&HistoricalAwards=false
• 关键词: CAREER; Machine; Learning; Methods; Statistical

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

职业：单一网络领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不同的学习场景（即，在链接节点的属性之间存在统计依赖关系）。在第一个场景中，领域由一群结构化示例（例如，化合物）组成，随着结构化示例数量的增加，我们可以渐近地推理学习算法。在第二种情况下，域由一个单一的、可能无限大小的网络（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，它们也对应于从同一底层网络中提取的子网络，因此可能是依赖的。虽然统计关系学习领域的估计和推理方法已经成功地应用于单网络领域，但这些算法最初是为网络群体开发的，因此在单网络中学习和推理的理论基础不足。这项工作的重点是为单个网络域开发健壮的统计方法——因为关于复杂系统的许多大型网络数据集很少有几个子网可用于模型估计和评估。具体而言，该项目的目标包括：(1)加强单网络领域学习的理论基础；(2)创建准确的方法来确定发现的模式和特征的意义；(3)制定新的模型选择和评估方法；(4)根据单网络领域的独特特征开发改进的网络学习和预测方法。该研究将加深我们对影响网络分析方法性能的机制的理解，并推动复杂网络领域新方法的发展。扩大机器学习技术在单一网络领域的适用性可能会对广泛的领域（例如，心理学，通信，教育，政治学）产生变革性影响，目前的方法将研究限制在双组或小组设置中的过程调查。此外，项目结果将作为计算机科学在更广泛的网络科学背景下的应用示例，这将吸引和留住那些可能不参与传统计算机科学主题的学生。欲了解更多详情，请访问：http://www.cs.purdue.edu/homes/neville/research-nsf-career.html