BIGDATA: F: Latent Structure and Dynamics of Big Data

BIGDATA：F：大数据的潜在结构和动态

基本信息

批准号：
1741355
负责人：
Dmitri Krioukov
金额：
$ 90万
依托单位：
Northeastern University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1741355&HistoricalAwards=false
关键词：
BIGDATA Latent Structure Dynamics Big

项目摘要

Big data poses big challenges. Perhaps the biggest challenge is to extract small but useful information from big noisy data. What approach should be used to do that, and for what data, so that this extraction is scalable, and yields not spurious artifacts but provably reliable predictive knowledge? Numerous data science applications are blocked on these questions. For example, the prediction and control of opinions, (fake) news, and (mis)information is a practical problem that is becoming of increasingly high and broad impact these days of pervasion of online social media into everyday human life. This particular problem is largely blocked on general impossibility of disentangling those who naturally bond with others like themselves from those influenced by peers in social networks, except in some specific settings. The specific settings of this project -- real networks with latent-space structure -- are exactly the settings in which these theoretical and practical difficulties can be resolved.The project will make a series of contributions in two areas. First, it will resolve a long-standing problem of obtaining a class of random graph models satisfying four requirements of realism: sparsity, exchangeability, projectivity and unbiasedness/maximum-entropy. Within this class, a set of graph-structural properties will be determined such that unbiased random graphs that have these properties are proved to have latent-geometric structure, thus rigorously linking discrete combinatorial structure of random graphs to smooth geometry of latent manifolds. The framework that the project will develop to prove this, will be quite general and applicable to other types of big data. The properties responsible for latent geometricity of random graphs are expected to characterize many real networks, meaning that such networks will be guaranteed to have latent geometries. The second part of the project will focus on developing scalable algorithms and software, with optimal computational complexity scaling linearly with the data size, and with proved accuracy guarantees, to learn the latent structure of a real network if the network has it, and apply these algorithms to large real networks. The outcomes of this latent-geometric learning will make it possible to map dynamical processes in real networks, such as spreading phenomena in social networks, to latent dynamics, while the knowledge of latent statistical factors behind this dynamic can then be used to predict and control it in practice with known accuracy bounds.

大数据带来了巨大的挑战。也许最大的挑战是从大的噪声数据中提取小但有用的信息。应该使用什么方法来做到这一点，以及对于什么数据，以便这种提取是可扩展的，并且产生的不是虚假的工件，而是可证明可靠的预测知识？许多数据科学应用程序都因这些问题而受到阻碍。例如，对观点、（假）新闻和（错误）信息的预测和控制是一个实际问题，随着在线社交媒体渗透到人类日常生活中，这个问题的影响变得越来越大和广泛。这个特殊问题在很大程度上受到阻碍，因为一般不可能将那些自然地与像他们一样的其他人联系在一起的人与那些受社交网络中的同龄人影响的人分开，除了在某些特定的环境中。该项目的具体设置——具有潜在空间结构的真实网络——正是能够解决这些理论和实践困难的设置。该项目将在两个领域做出一系列贡献。首先，它将解决一个长期存在的问题，即获得一类满足现实主义四个要求的随机图模型：稀疏性、可交换性、投影性和无偏性/最大熵。在此类中，将确定一组图结构属性，从而证明具有这些属性的无偏随机图具有潜在几何结构，从而将随机图的离散组合结构与潜在流形的平滑几何结构严格联系起来。该项目为证明这一点而开发的框架将非常通用，适用于其他类型的大数据。负责随机图潜在几何性的属性预计将表征许多真实网络，这意味着此类网络将保证具有潜在几何性。该项目的第二部分将专注于开发可扩展的算法和软件，最佳计算复杂度随数据大小线性扩展，并具有经过验证的准确性保证，以学习真实网络的潜在结构（如果网络具有），并应用这些结构大型真实网络的算法。这种潜在几何学习的结果将使将真实网络中的动态过程（例如社交网络中的传播现象）映射到潜在动态成为可能，而这种动态背后的潜在统计因素的知识可以用于预测和控制它在实践中具有已知的精度范围。

项目成果

期刊论文数量（31）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Geohyperbolic Routing and Addressing Schemes

DOI：
10.1145/3138808.3138811
发表时间：
2017-03
期刊：
ACM SIGCOMM Computer Communication Review
影响因子：
2.8
作者：
Ivan Voitalov;R. Aldecoa;Lan Wang;D. Krioukov
通讯作者：
Ivan Voitalov;R. Aldecoa;Lan Wang;D. Krioukov

Integration of Molecular Interactome and Targeted Interaction Analysis to Identify a COPD Disease Network Module.

DOI：
10.1038/s41598-018-32173-z
发表时间：
2018-09-27
期刊：
Scientific reports
影响因子：
4.6
作者：
Sharma A;Kitsak M;Cho MH;Ameli A;Zhou X;Jiang Z;Crapo JD;Beaty TH;Menche J;Bakke PS;Santolini M;Silverman EK
通讯作者：
Silverman EK

Machine learning in the string landscape