This $600,000 federal Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070) supports research to develop efficient training methods for Dynamic Graph Neural Network (DGNN) models on large-scale, time-varying graphs. The project aims to create innovative approaches for graph partitioning, sampling, caching, and training to enable highly scalable and efficient DGNN execution on time-varying graphs, which are...
The University of Virginia (UVA) received a $600,000 Project Grant from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Program (CFDA 47.070) to advance federated graph machine learning (FGML) techniques. The project aims to 1) address data heterogeneity challenges in FGML, 2) develop novel algorithms to tackle label deficiency issues, and 3) strengthen data privacy protection for node attributes and graph structures. The research will produce...
The National Science Foundation (NSF) awarded a $179,055 Project Grant to the University of Virginia under the Computer and Information Science and Engineering (CFDA 47.070) program. The grant supports collaborative research to develop privacy-preserving algorithms for fundamental problems in graph mining and network science. The project aims to create scalable, accurate graph differential privacy algorithms for applications like healthcare, social networks, finance, and computational...
The University of Illinois was awarded a $1,201,199 project grant from the National Science Foundation Division of Computer and Network Systems to support research titled "SATC: CORE: MEDIUM: PRINCIPLED FOUNDATIONS FOR THE DESIGN AND EVALUATION OF GRAPH-BASED HOST INTRUSION DETECTION SYSTEMS." The award period is from October 1, 2021 to September 30, 2025. The grant supports research and development activities under the NSF's Computer and Information Science and Engineering program...
The National Science Foundation (NSF) awarded a $299,973 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) federal grant program to the Rector & Visitors Of The University Of Virginia (UVA), doing business as University of Virginia. The grant, titled "COLLABORATIVE RESEARCH: OAC CORE: DISTRIBUTED GRAPH LEARNING CYBERINFRASTRUCTURE FOR LARGE-SCALE SPATIOTEMPORAL PREDICTION", aims to develop a comprehensive set of graph construction and...
This $125,000 Project Grant awarded by the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences program (CFDA 47.049) supports a collaborative research project on developing improved graph neural network (GNN) algorithms for threat detection. The key research objectives are to: 1) maintain accuracy with deep GNNs, 2) enable GNN training with limited data, and 3) reduce computational costs for training and deploying deep GNNs with...
The National Science Foundation awarded a $499,979 project grant to the George Washington University under the Computer and Information Science and Engineering program (CFDA 47.070). The grant will support research towards developing high-performance machine learning techniques on graphs from October 1, 2021 to September 30, 2024. The Computer and Information Science and Engineering program aims to advance computing and informatics research and education. This award will further those goals by...
The National Science Foundation awarded a $200,657 Project Grant under the Computer and Information Science and Engineering (CFDA #47.070) program to the University of Maryland, College Park (UMD). The grant supports collaborative research to develop private algorithms for fundamental problems in graph mining and network science, such as subgraph detection, node ranking, community detection, and studying properties of graph dynamical systems like epidemic spread. The research aims to create...
This $350,000 National Science Foundation project grant supports statistical modeling research for complex networks at the University of Michigan from September 2022 through August 2025. Funded through the NSF's Mathematical and Physical Sciences program (CFDA 47.049), the award aims to develop new statistical methodologies and theory to incorporate higher-order structures into network modeling. Specifically, the investigators will study leveraging subgraphs and other higher-order structures...
The National Science Foundation (NSF) has awarded a $300,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the Old Dominion University Research Foundation (Odurf) to develop a new holistic and standardized graph learning framework for open-world and streaming network learning. Key objectives include characterizing complex and evolving graph data representations, identifying the emergence of new classes, and generalizing graph models across...