Zhuo Feng (ECE/ICC) is Principal Investigator on a project that has received a $500,000 research and development grant from the National Science Foundation. This potential three-year project is titled, “SHF: Small: Spectral Reduction of Large Graphs and Circuit Networks.”
This research project will investigate a truly-scalable yet unified spectral graph reduction approach that allows reducing large-scale, real-world directed and undirected graphs with guaranteed preservation of the original graph spectra. Unlike prior methods that are only suitable for handling specific types of graphs (e.g. undirected or strongly-connected graphs), this project uses a more universal approach and thus will allow for spectral reduction of a much wider range of real-world graphs that may involve billions of elements:
- spectrally-reduced social (data) networks allow for more efficiently modeling, mining and analysis of large social (data) networks;
- spectrally-reduced neural networks allow for more scalable model training and processing in emerging machine learning tasks;
- spectrally-reduced web-graphs allow for much faster computations of personalized PageRank vectors;
- spectrally-reduced integrated circuit networks will lead to more efficient partitioning, modeling, simulation, optimization and verification of large chip designs, etc.
From Tech Today, June 21, 2019