CAREER: Novel Approaches for Mining Large and Complex Networks

这个职业项目包括一个综合的研究、教育和推广计划，专注于开发挖掘大型、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，但网络分析的方法论发展仍处于早期阶段。该项目解决了对推进大型复杂网络分析至关重要的基本问题。这些挑战是由社会、生物和医疗领域的真实应用驱动的。与研究计划相辅相成的是一项全面的教育和推广计划，重点放在三个方面：(1)开发新的跨学科课程；(2)本科生直接参与研究项目；(3)推广活动，包括针对K-12学校的STEM计划。将鼓励代表人数不足的学生参与这一项目。该项目的研究目标是显著提高大型网络分析的可靠性和效率。该项目有三个研究目标。(1)提出了一种新的基于记忆的随机游走邻近度度量方法，能够有效地捕捉节点间的相似性。邻近度度量对于许多高级网络分析任务是至关重要的。为制定的措施提供了严密的理论基础。(2)研究了双网络模型及其应用。双网络模型在现实世界中有着广泛的应用。本项目将对跨网络节点集查询的具体问题进行研究。我们将探索数值和算法两种方法。(3)设计健壮、灵活的多网络聚类排序算法。重点将放在一种新颖的多网络模型上，即一种网络网络，它允许我们整合领域相似性来提高算法的性能。

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.