6 citations · 10 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 3 cited
SF-SGL: Solver-Free Spectral Graph Learning from Linear Measurements
Ying Zhang, Zhiqiang Zhao, Zhuo Feng
This work introduces a highly-scalable spectral graph densification framework (SGL) for learning resistor networks with linear measurements, such as node voltages and currents. We…
cs.DS2020★ 6 cited
SF-GRASS: Solver-Free Graph Spectral Sparsification
Ying Zhang, Zhiqiang Zhao, Zhuo Feng
Recent spectral graph sparsification techniques have shown promising performance in accelerating many numerical and graph algorithms, such as iterative methods for solving large sp…
cs.DS2018★ 1 cited
A Unified Approach to Scalable Spectral Sparsification of Directed Graphs
Ying Zhang, Zhiqiang Zhao, Zhuo Feng
Recent spectral graph sparsification research allows constructing nearly-linear-sized subgraphs that can well preserve the spectral (structural) properties of the original graph, s…