III: Small: Statistical Low-Rank Factorization Tools for Ecological Network Link Prediction

III：小：生态网络链路预测的统计低秩分解工具

• 批准号: 1910118
• 负责人: Rebecca Hutchinson
• 金额: $ 50万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910118&HistoricalAwards=false
• 关键词: III; Small; Statistical; Low; Rank

In many kinds of networks, it is interesting to try to predict which links exist or could exist even though they have not been observed so far. For example, in order to make recommendations, Netflix predicts new links between users and movies - movies that the user has not watched yet but is likely to enjoy based on the ratings that similar users have given to similar movies. Link prediction has been well studied for movie and product recommendation, but link prediction between species in an ecosystem has received less attention. Understanding and predicting unobserved links in ecological networks is important for natural resource management, species monitoring, and crop production. Link prediction in ecological networks has some similarities to link prediction for recommendation but also some sharp contrasts. In particular, ecological network data contain more errors than user-movie or user-product networks because field sampling cannot capture all of the true links in the network. Ecological networks are also influenced by factors like the abundance of species and competition for limited resources, and these networks change over time and space. Led by a cross-disciplinary team of investigators, this project will thoroughly address the unique challenges that arise in ecological networks. It will put forth a unified framework including modeling tools and computational infrastructure for analyzing ecological networks from incomplete data over time and space.In terms of theory and methods, many key aspects of link prediction in ecological networks such as statistical models, scalable algorithms, and multimodality integration are still poorly understood. This project will provide a suite of analytical and computational tools addressing these challenges. The research will follow three synergistic directions. First, basic framework development will produce statistical models and optimization algorithms that account for the unique traits of ecological networks. Second, the researchers will put forth solutions for highly challenging issues like link prediction under limited resources and species competition. Third, the team will provide a systematic evaluation plan for the problems of interest. Designing effective statistical models and robust, scalable algorithms for ecological networks is well-motivated for both modern ecology research and computer science. This project will lay out the foundations for ecological network analytics by leveraging modern data science tools such as low-rank matrix/tensor factorization, graphical models, and neural networks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在许多类型的网络中，尝试预测哪些链接存在或可能存在是很有趣的，即使它们到目前为止还没有被观察到。例如，为了进行推荐，Netflix预测用户与电影之间的新链接-用户尚未观看但可能会根据类似用户对类似电影的评级来欣赏的电影。链接预测在电影和产品推荐中已经得到了很好的研究，但生态系统中物种之间的链接预测却很少受到关注。理解和预测生态网络中未观测到的联系对于自然资源管理、物种监测和作物生产都很重要。生态网络中的链接预测与用于推荐的链接预测有一些相似之处，但也有一些鲜明的对比。特别是，生态网络数据包含更多的错误比用户-电影或用户-产品网络，因为现场采样不能捕捉网络中的所有真正的链接。生态网络还受到物种丰富度和对有限资源的竞争等因素的影响，这些网络随着时间和空间而变化。在跨学科研究团队的领导下，该项目将彻底解决生态网络中出现的独特挑战。它将提出一个统一的框架，包括建模工具和计算基础设施，用于从时间和空间上的不完整数据分析生态网络。在理论和方法方面，生态网络中的链接预测的许多关键方面，如统计模型，可扩展算法和多模态集成仍然知之甚少。该项目将提供一套分析和计算工具来应对这些挑战。研究将遵循三个协同方向。首先，基本框架的开发将产生统计模型和优化算法，说明生态网络的独特特征。第二，研究人员将为资源有限和物种竞争下的链接预测等极具挑战性的问题提出解决方案。 第三，团队会针对感兴趣的问题提供系统的评估方案。为生态网络设计有效的统计模型和鲁棒的、可扩展的算法对于现代生态学研究和计算机科学都是很有意义的。该项目将利用现代数据科学工具，如低秩矩阵/张量因子分解、图形模型和神经网络，为生态网络分析奠定基础。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

StatEcoNet: Statistical Ecology Neural Networks for Species Distribution Modeling

• DOI: 10.1609/aaai.v35i1.16129
• 发表时间: 2021-02
• 期刊: ArXiv
• 作者: Eugene Seo;R. Hutchinson;Xiao Fu;Chelsea Li;Tyler A. Hallman;J. Kilbride;W. Robinson

Computing Large-Scale Matrix and Tensor Decomposition With Structured Factors: A Unified Nonconvex Optimization Perspective

• DOI: 10.1109/msp.2020.3003544
• 发表时间: 2020-06
• 期刊: IEEE Signal Processing Magazine
• 影响因子: 14.9
• 作者: Xiao Fu;Nico Vervliet;L. De Lathauwer;Kejun Huang;Nicolas Gillis

Benchmark Bird Surveys Help Quantify Counting Accuracy in a Citizen-Science Database

基准鸟类调查有助于量化公民科学数据库中的计数准确性

• DOI: 10.3389/fevo.2021.568278
• 发表时间: 2021
• 期刊: Frontiers in ecology and evolution
• 影响因子: 3
• 作者: Robinson, W. Douglas;Hallman, Tyler A.;Hutchinson, Rebecca A.
• 通讯作者: Hutchinson, Rebecca A.

A comparison of remotely sensed environmental predictors for avian distributions

鸟类分布遥感环境预测因子的比较

• DOI: 10.1007/s10980-022-01406-y
• 发表时间: 2022
• 期刊: Landscape Ecology
• 影响因子: 5.2
• 作者: Hopkins, Laurel M.;Hallman, Tyler A.;Kilbride, John;Robinson, W. Douglas;Hutchinson, Rebecca A.
• 通讯作者: Hutchinson, Rebecca A.

Spectrum Cartography via Coupled Block-Term Tensor Decomposition

• DOI: 10.1109/tsp.2020.2993530
• 发表时间: 2019-11
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Guoyong Zhang;Xiao Fu;Jun Wang;Xile Zhao;Mingyi Hong