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AF: Small: Scalable Algorithms for Data and Network Analysis

AF：小型：用于数据和网络分析的可扩展算法

基本信息

批准号：
1815254
负责人：
Shanghua Teng
金额：
$ 50万
依托单位：
University of Southern California
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-06-01 至 2022-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815254&HistoricalAwards=false
关键词：
AF Small Scalable Algorithms Data

项目摘要

Data-based decision-making involving big input data sets require a lot of time for algorithms to process them. In computer science, efficient algorithms are generally considered to be the ones whose running time does not increase "too fast" when input size grows. "Scalable algorithms" are among the most efficient algorithms, because their running time is required to be nearly linear or even sub-linear with respect to the problem size. In other words, their complexity scales gracefully when the input size scales up. In the age of Big Data, efficient algorithms are in higher demand now more than ever before. While big data takes us into the asymptotic world envisioned by the pioneers of computer science, the explosive growth of problem size has also significantly challenged the classical notion of efficient algorithms: Algorithms that used to be considered efficient, according to the traditional polynomial-time characterization, may no longer be adequate for solving today's problems. It is not just desirable, but essential, that efficient algorithms should be scalable. Thus, scalability, instead of polynomial-time computability, should be elevated to the central complexity notion for characterizing efficient computation, and this will be the focus of algorithm design for network sciences and big data. This project will focus on the design and analysis of scalable algorithms. One of its primary objective is to build bridges between the area of algorithm design and the fields of network sciences and machine learning. If successful, the project will help to provide a rigorous algorithmic framework for designing new scalable data and network analysis algorithms. By focusing on notions of algorithmic efficiency --- such as scalability --- that respect the sensibilities of researchers in these disciplines as to what constitutes a practical algorithm in the age of big data, this project aims to increase the value of theoretical analyses to researchers in these fields. As this project combines ideas from many disciplines within one coherent research effort, lectures and tutorials presented on the fruits of the project will help cross-fertilize the disciplines within its scope. The development of theoretical algorithms that might have practical applicability should simplify education in algorithms for students beyond theoretical computer science, and allow discussion of practically important heuristics at early stages of computer science education. The interdisciplinary nature of this research will enable broader engagements with PhD students and researchers in network sciences, machine learning, numerical analysis, and social science and hence will enhance the chance to be successful in supporting and working with women and minority researchers.The technical goal of this project is to systematically extend the family of algebraic, numerical, and combinatorial techniques from the recent breakthroughs in Laplacian linear solvers and max-flows/min-cuts, to a wide-range of problems that arise in network analysis, data mining, and machine learning. This project aims to show that these techniques --- including advanced sampling, sparsification, and local exploration of networks --- will play increasing roles in improving algorithmic scalability. It contains several open questions and conjectures ranging from network analysis to distribution sampling to social influences towards this goal. By providing new algorithmic insights into various dynamic processes over graphs, the project also aims to improve the understanding of network facets beyond static graph structures in order to develop better algorithmic theory for network sciences.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.

涉及大数据集的基于数据的决策需要算法处理它们需要大量时间。在计算机科学中，高效的算法通常被认为是那些运行时间不会随着输入大小的增长而“过快”增加的算法。“可伸缩算法”是最有效的算法之一，因为它们的运行时间要求相对于问题大小几乎是线性的，甚至是次线性的。换言之，当输入大小增大时，它们的复杂性就可以很好地扩展。在大数据时代，对高效算法的需求比以往任何时候都更高。虽然大数据将我们带入了计算机科学先驱设想的渐近世界，但问题规模的爆炸性增长也对高效算法的经典概念提出了重大挑战：根据传统的多项式时间特征，过去被认为有效的算法可能不再足以解决当今的问题。高效的算法应该是可扩展的，这不仅是可取的，而且是必要的。因此，可伸缩性，而不是多项式时间的可计算性，应该上升为表征高效计算的中心复杂性概念，这将是网络科学和大数据算法设计的重点。本项目将专注于可伸缩算法的设计和分析。它的主要目标之一是在算法设计领域与网络科学和机器学习领域之间建立桥梁。如果成功，该项目将有助于为设计新的可伸缩数据和网络分析算法提供严格的算法框架。通过关注算法效率的概念--例如可伸缩性--尊重这些学科的研究人员对于大数据时代什么构成实际算法的敏感性，该项目旨在增加理论分析对这些领域的研究人员的价值。由于这个项目在一个连贯的研究工作中结合了许多学科的想法，关于该项目成果的讲座和教程将有助于在其范围内交叉培养各学科。开发可能具有实际适用性的理论算法应该简化学生在理论计算机科学之外的算法教育，并允许在计算机科学教育的早期阶段讨论实际重要的启发式方法。这项研究的跨学科性质将使博士生和研究人员能够在网络科学、机器学习、数值分析和社会科学方面进行更广泛的接触，从而增加在支持和与女性和少数民族研究人员合作方面取得成功的机会。该项目的技术目标是系统地将代数、数值和组合技术家族从最近在拉普拉斯线性求解器和最大流/最小切割方面的突破扩展到网络分析、数据挖掘和机器学习中出现的广泛问题。这个项目旨在表明这些技术-包括高级采样、稀疏和网络的局部探索--将在提高算法可伸缩性方面发挥越来越大的作用。它包含几个开放的问题和猜想，从网络分析到分布抽样，再到对这一目标的社会影响。通过提供对图形上各种动态过程的新的算法洞察，该项目还旨在提高对静态图形结构以外的网络方面的理解，以便为网络科学开发更好的算法理论。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Quantum-Inspired Combinatorial Games: Algorithms and Complexity

受量子启发的组合游戏：算法和复杂性

DOI：
10.4230/lipics.fun.2022.11
发表时间：
2022
期刊：
Fun with Algorithms
影响因子：
0
作者：
Kyle G. Burke;Matthew Ferland;S. Teng
通讯作者：
S. Teng

Computational Analyses of the Electoral College: Campaigning Is Hard But Approximately Manageable

选举团的计算分析：竞选活动很困难，但大致可控

DOI：
10.1609/aaai.v35i6.16668
发表时间：
2021
期刊：
Proceedings of the AAAI Conference on Artificial Intelligence
影响因子：
0
作者：
Dehghani, Sina;Saleh, Hamed;Seddighin, Saeed;Teng, Shang-Hua
通讯作者：
Teng, Shang-Hua

A graph-theoretical basis of stochastic-cascading network influence: Characterizations of influence-based centrality

DOI：
10.1016/j.tcs.2020.04.016
发表时间：
2020-07
期刊：
Theor. Comput. Sci.
影响因子：
0
作者：
Wei Chen;S. Teng;Hanrui Zhang
通讯作者：
Wei Chen;S. Teng;Hanrui Zhang

Optimal Space-Depth Trade-Off of CNOT Circuits in Quantum Logic Synthesis.

量子逻辑综合中 CNOT 电路的最佳空间深度权衡。

DOI：
发表时间：
2020
期刊：
Proceedings of the annual ACMSIAM symposium on discrete algorithms
影响因子：
0
作者：
Jiang, Jiaqing;Sun, Xiaoming;Teng, Shang-Hua;Wu, Bujiao;Wu, Kewen;Zhang, Jialin
通讯作者：
Zhang, Jialin

Capturing Complementarity in Set Functions by Going Beyond Submodularity/Subadditivity.

通过超越子模/子可加性来捕获集合函数中的互补性。