SHF: Small: Towards High-Performance Machine Learning on Graphs

SHF：小型：迈向图上的高性能机器学习

基本信息

批准号：
2127207
负责人：
H. Howie Huang
金额：
$ 50万
依托单位：
George Washington University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2127207&HistoricalAwards=false
关键词：
SHF Small Towards Performance Machine

项目摘要

The world has become increasingly intertwined through social networks, smart phones, and more generally cyber-physical systems. Connected people, devices, and systems introduce a pressing research challenge, that is, the need of understanding the relationships between different entities in dynamic networks. As graphs with vertices and edges are a natural representation of such relationships, machine learning on these graphs (networks) is beneficial to a variety of applications spanning from biology, finance, to social science. Importantly, these networks grow exponentially in size. As a result, one needs to not only store and manage large-scale, dynamic graph datasets, but also support fast execution of machine-learning tasks.This project designs and develops a new set of high-performance machine-learning algorithms and systems for big graph datasets. The research goal is achieved through systems-level innovations for enabling graph-based machine learning on high-performance computing systems, coupled with the development of new scalable, robust algorithms for graph extraction and learning. The new graph learning system will significantly advance the state of the art in machine learning on graphs, delivering new tools to an array of scientific disciplines from social to life sciences. A number of undergraduate and graduate students, including those from under-represented groups, will participate in this project. The research results will be disseminated as open-source projects, as well as technical publications in major conferences and scientific journals.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.

通过社交网络，智能手机和更普遍的网络物理系统，世界越来越多地交织在一起。连接的人员，设备和系统引入了紧迫的研究挑战，即需要了解动态网络中不同实体之间的关系。由于具有顶点和边缘的图形是这种关系的自然表示，因此这些图表上的机器学习（网络）对从生物学，金融到社会科学的各种应用都有益。重要的是，这些网络的大小成倍增长。结果，一个人不仅需要存储和管理大规模的动态图形数据集，而且还需要支持机器学习任务的快速执行。该项目设计并开发了一组新的高性能机器学习算法和大图数据集的系统。研究目标是通过系统级创新来实现的，以使基于图形的机器学习高性能计算系统，并开发新的可扩展，可稳定的鲁棒算法用于图形提取和学习。新的图形学习系统将在图形上的机器学习中大大提高艺术的状态，从而为一系列科学学科从社会科学到生命科学提供新的工具。许多本科生和研究生，包括来自代表性不足的小组的本科生和研究生将参加该项目。研究结果将作为开源项目以及主要会议和科学期刊的技术出版物传播。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估标准来通过评估来支持的。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Graggle: A Graph-based Approach to Document Clustering

DOI：
10.1109/bigdata55660.2022.10020645
发表时间：
2022-12
期刊：
2022 IEEE International Conference on Big Data (Big Data)
影响因子：
0
作者：
I. J. King;H. H. Huang-H.
通讯作者：
I. J. King;H. H. Huang-H.

Pikachu: Temporal Walk Based Dynamic Graph Embedding for Network Anomaly Detection

DOI：
10.1109/noms54207.2022.9789921
发表时间：
2022-04
期刊：
NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium
影响因子：
0
作者：
Ramesh Paudel;Huimin Huang
通讯作者：
Ramesh Paudel;Huimin Huang

TLPGNN: A Lightweight Two-Level Parallelism Paradigm for Graph Neural Network Computation on GPU

DOI：
10.1145/3502181.3531467
发表时间：
2022-06
期刊：
Proceedings of the 31st International Symposium on High-Performance Parallel and Distributed Computing
影响因子：
0
作者：
Qiang Fu;Yuede Ji;Huimin Huang
通讯作者：
Qiang Fu;Yuede Ji;Huimin Huang

Euler: Detecting Network Lateral Movement via Scalable Temporal Graph Link Prediction

Euler：通过可扩展的时间图链接预测检测网络横向运动