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Research questionHow can sparse balanced signed graph Laplacians be learned efficiently while preserving positive-graph spectral tools?Signed graphs represent both correlations and anti-correlations, but enforcing balanced sign structure while learning a sparse Laplacian from data creates computational constraints. Balance also matters because it determines whether positive-graph spectral methods can be reused.
Machine Learning
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Statistical Machine Learning
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Latest papersRecent research connected to this question, newest first.Efficient Learning of Balanced Signed Graphs via Sparse Linear ProgrammingThe paper formulates sign-constrained linear programs for individual Laplacian columns and solves them with a sparse ADMM-based method. It provides objective-monotonicity and finite-termination analysis, with experiments on synthetic and real-world datasets; the reported evidence concerns learning quality and reuse of positive-graph spectral tools.research paper · Sep 1, 2026
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