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

SHF：小型：迈向图上的高性能机器学习

• 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

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，世界变得越来越相互交织。连接的人，设备和系统引入了一个紧迫的研究挑战，即需要了解动态网络中不同实体之间的关系。由于具有顶点和边的图是这种关系的自然表示，因此这些图（网络）上的机器学习有利于从生物学，金融到社会科学的各种应用。重要的是，这些网络的规模呈指数级增长。因此，不仅需要存储和管理大规模的动态图数据集，还需要支持机器学习任务的快速执行。本项目设计并开发了一套新的高性能机器学习算法和系统，用于大图数据集。研究目标是通过系统级创新来实现的，以在高性能计算系统上实现基于图的机器学习，再加上开发新的可扩展的，强大的图提取和学习算法。新的图学习系统将显著推进图上机器学习的最新技术，为从社会科学到生命科学的一系列科学学科提供新工具。一些本科生和研究生，包括来自代表性不足群体的学生，将参加这一项目。研究成果将作为开源项目以及主要会议和科学期刊上的技术出版物传播。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

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)
• 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
• 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
• 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

• DOI: 10.14722/ndss.2022.24107
• 发表时间: 2022
• 期刊: Proceedings of 29th Network and Distributed System Security Symposium (NDSS
• 作者: King, Isaiah;Huang, H. H.
• 通讯作者: Huang, H. H.