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

该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的低直径集群，这些集群受到测量误差和不完整信息的影响。这项研究的重点是一种新的模型，用于检测社交和生物网络中的集群，称为“k俱乐部”。“作为本研究的一部分，一些关键的创新包括使用金融工程的风险度量，用于量化由于大数据网络模型的测量误差和不确定性而导致的集群凝聚力损失。这一措施代表了在最坏情况下的高损失的下行风险，导致风险规避的数学模型，便于检测更有可能是应用显着的集群。 这项研究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，以及公开的社交和金融网络数据集进行验证。如果成功，这项工作将使生物学家能够更好地了解复杂的生物网络，加速发现并降低实验成本。该研究有可能刺激基础发现，从而导致生物医学，农业和生物能源的进步，并产生积极的社会影响。高中数学教师将参与获得大规模网络/数据分析的第一手经验，以及通过主动学习练习识别和转移适当的材料到他们的课堂。加强少数群体的参与和专业发展是该项目的主要外联目标。来自代表性不足群体的研究生和本科生将积极寻求参与研究，并希望成为国家科学委员会推荐的教授，并与美国竞争再授权法案保持一致。

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.