Presents signed forest matrix theorem and GSCF/FMDE algorithms achieving O(n) forest generation and O(ln) diagonal estimation for signed graphs up to 20M nodes.
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cs.SI 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
New variance-reduced sampling algorithms SCF, SCFV, and SCFV+ compute forest matrix diagonals more efficiently than Laplacian solvers with error guarantees and linear time in nodes for both undirected and directed graphs.
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Fast Estimation for Forest Matrix of Signed Graphs
Presents signed forest matrix theorem and GSCF/FMDE algorithms achieving O(n) forest generation and O(ln) diagonal estimation for signed graphs up to 20M nodes.
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Efficient Computation for Diagonal of Forest Matrix via Variance-Reduced Forest Sampling
New variance-reduced sampling algorithms SCF, SCFV, and SCFV+ compute forest matrix diagonals more efficiently than Laplacian solvers with error guarantees and linear time in nodes for both undirected and directed graphs.