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SPX: Collaborative Research: SANDY: Sparsification-Based Approach for Analyzing Network Dynamics

SPX：协作研究：SANDY：基于稀疏化的网络动态分析方法

基本信息

批准号：
1916084
负责人：
Sanjukta Bhowmick
金额：
$ 17.96万
依托单位：
University of North Texas
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1916084&HistoricalAwards=false
关键词：
SPX Collaborative Research SANDY Sparsification

项目摘要

The goal of this three-year project, Sparsification-based Approach for Analyzing Network Dynamics (SANDY), is to develop a suite of scalable parallel algorithms for updating dynamic networks for different problems that can be executed on a wide range of HPC platforms. Dynamic network analysis will enable researchers to study the evolution of complex systems in diverse disciplines, such as bioinformatics, social sciences, and epidemiology. The SANDY project is expected to initiate a new direction of research in developing parallel dynamic network algorithms that will benefit multiple analysis objectives (e.g., motif finding and network alignment) and application domains (e.g., epidemiology, health care). Research findings will be integrated into courses on network analysis, parallel algorithms, and bioinformatics offered at the three collaborating institutions. The PIs will collaborate with high schools to deliver talks on network theory, and encourage women and minority students to pursue IT-related careers. To develop efficient and scalable parallel algorithms, the PIs propose to use an elegant technique, called graph sparsification, that expresses graph algorithms in a reduction-like fashion. The formal steps to parallelization, as guided by the graph sparsification framework, provide a template for creating provably correct parallel algorithms for dynamic networks. The proposed algorithms will address the dual needs of portability and performance optimization. The framework will further provide a mechanism for combining high level (e.g., static and dynamic graph partitioning) and low level (e.g., dataflow algorithms) tuning strategies to ensure high performance and scalability for various parallel architectures by considering such factors as scalability, time, memory, and energy efficiency.

这个为期三年的项目，基于稀疏化的方法分析网络动态（桑迪）的目标是开发一套可扩展的并行算法，用于更新动态网络，以解决各种不同的问题，这些问题可以在各种HPC平台上执行。动态网络分析将使研究人员能够在不同学科中研究复杂系统的进化，如生物信息学，社会科学和流行病学。桑迪项目有望在开发并行动态网络算法方面开创一个新的研究方向，这将有利于多个分析目标（例如，基序发现和网络比对）和应用领域（例如，流行病学、卫生保健）。研究结果将被整合到三个合作机构提供的网络分析，并行算法和生物信息学课程中。PI将与高中合作，提供关于网络理论的讲座，并鼓励女性和少数民族学生从事与IT相关的职业。为了开发高效和可扩展的并行算法，PI建议使用一种称为图稀疏化的优雅技术，该技术以类似约简的方式表达图算法。并行化的正式步骤，由图稀疏化框架指导，为创建可证明正确的动态网络并行算法提供了一个模板。所提出的算法将解决可移植性和性能优化的双重需求。该框架将进一步提供一种用于将高级（例如，静态和动态图划分）和低级（例如，通过考虑诸如可扩展性、时间、存储器和能量效率之类的因素来确保各种并行架构的高性能和可扩展性。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Single-Source Shortest Path Tree for Big Dynamic Graphs

大动态图的单源最短路径树

DOI：
10.1109/bigdata.2018.8622042
发表时间：
2018
期刊：
IEEE International Conference on Big Data
影响因子：
0
作者：
Riazi, Sara;Srinivasan, Sriram;Das, Sajal K.;Bhowmick, Sanjukta;Norris, Boyana
通讯作者：
Norris, Boyana

Partitioning Communication Streams Into Graph Snapshots

将通信流划分为图形快照

DOI：
10.1109/tnse.2022.3223614
发表时间：
2022
期刊：
IEEE Transactions on Network Science and Engineering
影响因子：
6.6
作者：
Wendt, Jeremy D.;Field, Richard V.;Phillips, Cynthia A.;Prasadan, Arvind;Wilson, Tegan;Soundarajan, Sucheta;Bhowmick, Sanjukta
通讯作者：
Bhowmick, Sanjukta

Applying a Probabilistic Infection Model for studying contagion processes in contact networks