CAREER: Novel Approaches for Mining Large and Complex Networks

职业：挖掘大型复杂网络的新方法

• 批准号: 1707548
• 负责人: Xiang Zhang
• 金额: $ 49.92万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-15 至 2023-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1707548&HistoricalAwards=false
• 关键词: CAREER; Novel; Approaches; Mining; Large

This CAREER project includes an integrated research, education, and outreach program that focusses on the development of novel methods for mining large, complex networks. Networks (graphs) are ubiquitous in real-world applications. Although successful, the methodology development for network analytics is still in its early stage. This project addresses fundamental questions essential to the advancement of large and complex network analytics. These challenges are driven by real-world applications in social, biological, and medical domains. The research plan is complemented by a comprehensive education and outreach plan focussed on three elements: (1) the development of new interdisciplinary courses; (2) direct undergraduate involvement in the research projects; and (3) outreach activities including the STEM program targeting K-12 schools. Underrepresented students will be encouraged to participate in this project. The research goal of the project is to significantly extend the reliability and efficiency of large network analysis. The project has three research aims. (1) Develop novel memory-based random walk proximity measures that can effectively capture the similarity between nodes. Proximity measure is of fundamental importance for many advanced network analysis tasks. A rigorous theoretical foundation will be provided for the developed measures. (2) Study the dual-network model and its applications. The dual-network model has a wide range real-world applications. The specific problem of cross-network node set query will be investigated in the project. Both numerical and algorithmic approaches will be explored. (3) Design robust and flexible multi-network algorithms for clustering and ranking. The focus will be on a novel multi-network model, a network of networks, which allows us to integrate domain similarities to improve the performance of the algorithms.

该项目包括一个综合的研究，教育和推广计划，重点是开发挖掘大型复杂网络的新方法。 网络（图）在现实世界的应用中无处不在。尽管取得了成功，但网络分析方法的开发仍处于早期阶段。该项目解决了对大型复杂网络分析的发展至关重要的基本问题。这些挑战是由社会、生物和医疗领域的实际应用所驱动的。该研究计划由一个全面的教育和推广计划补充，重点关注三个要素：（1）新的跨学科课程的开发;（2）直接参与研究项目的本科生;和（3）推广活动，包括针对K-12学校的STEM计划。将鼓励代表性不足的学生参加这一项目。该项目的研究目标是显著扩展大型网络分析的可靠性和效率。该项目有三个研究目标。(1)开发新的基于内存的随机游走邻近度测量方法，可以有效地捕获节点之间的相似性。邻近性度量对于许多高级网络分析任务具有根本的重要性。将为所开发的措施提供严格的理论基础。(2)研究了双网络模型及其应用。双网模型在现实世界中有着广泛的应用。跨网络节点集查询的具体问题将在项目中进行研究。数值和算法的方法将进行探讨。(3)为聚类和排名设计健壮灵活的多网络算法。重点将放在一个新的多网络模型，网络的网络，它允许我们整合域的相似性，以提高算法的性能。