Collaborative Research: Risk-Averse Cluster Detection in Network Models of Bigdata Under Measurement Uncertainty

这一合作研究奖的目标是建立一个全面的知识库，用于检测社会和生物大数据网络模型中受测量误差和信息不完全影响的小直径集群。这项研究的重点是一种新的模型，用于检测社会和生物网络中的集群，该模型被称为“k-Club”。作为这项研究的一部分，一些关键创新包括使用来自金融工程的风险度量，该度量用于量化由于作为大数据网络模型基础的度量误差和不确定性而导致的集群凝聚力损失。这一衡量标准代表了在最糟糕的情况下高损失的下行风险，从而导致了规避风险的数学模型，这些模型有助于检测更有可能对应用具有重大意义的集群。这项研究得出的模型和算法将使用实验室实验数据和生物科学合作者提供的专业知识以及公开可用的社会和金融网络数据集进行验证。如果成功，这项工作可以使生物学家更好地了解复杂的生物网络，加速发现并降低实验成本。这项研究有可能促进基础性发现，从而促进生物医学、农业和生物能源领域的进步，产生积极的社会影响。高中数学教师将接受在大规模网络/数据分析方面的第一手经验，并通过积极的学习练习确定适当的材料并将其转移到课堂上。加强少数群体的参与和专业发展是该项目的主要外展目标。来自代表性不足群体的研究生和本科生将被积极寻求参与研究，希望成为国家科学委员会推荐的教授，并与《美国竞争重新授权法案》保持一致。

The objective of this collaborative research award is to establish a comprehensive knowledge-base for detecting low-diameter clusters in network models of social and biological big-data that are subject to measurement errors and incomplete information. This research study focuses on a novel model for detecting clusters in social and biological networks called a "k-club." Some key innovations pursued as part of this study include the use of a risk measure from financial engineering that is used to quantify losses in cluster cohesiveness that results from the measurement errors and uncertainty that underlies such network models of big data. This measure represents the downside risk of high losses in the worst-case scenarios leading to risk-averse mathematical models that facilitate the detection of clusters that are more likely to be application significant. The models and algorithms that result from this study will be validated using laboratory experimental data and expertise provided by collaborators in biological sciences, and also by using publicly available social and financial network datasets.If successful, this work can enable biologists to better understand complex biological networks, accelerate discoveries and reduce experimental costs. The research has the potential to spur fundamental discoveries that can lead to advances in biomedicine, agriculture and bioenergy with positive societal impacts. High School mathematics teachers will be engaged to receive first-hand experience in large-scale network/data analysis as well as identify and transfer appropriate material to their classroom via active learning exercises. Enhancing minority participation and professional development are key outreach goals of this project. Graduate and undergraduate students from under-represented groups will be actively sought to become engaged in the research and hopefully the professoriate as recommended by the National Science Board and aligned with the America COMPETES Reauthorization Act.