arXiv Machine Learning
Stability Analysis of Signed Graphs under Structural Perturbations
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arXiv Machine Learning
Aug 4
Nonlinear Laplacians Improve Signed-Directed Graph Learning
arXiv:2608. 00836v1 Announce Type: new Abstract: While signed-directed graphs have been studied using linear Laplacians in the design of graph neural networks, relatively little research has focused on developing non-linear Laplacian operators for such networks.
arXiv Machine Learning
Sep 23
Signed Graph Pre-Training and Prompt Learning
arXiv:2609.25722v1 Announce Type: new Abstract: Signed graphs arise in trust--distrust networks, financial correlation systems, biological interaction graphs, and many other domains in which edges ca...
arXiv Machine Learning
Sep 10
Polarity-Asymmetric Structural Calibration for Link Sign Prediction
arXiv:2609.05896v1 Announce Type: cross Abstract: Link sign prediction (LSP) aims to infer the positive or negative polarity of unobserved links in signed networks. Signed Graph Neural Networks (SGNN...
arXiv Machine Learning
Jul 31
MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian
arXiv:2209. 00546v5 Announce Type: replace-cross Abstract: Signed and directed networks are ubiquitous in real-world applications.
arXiv Machine Learning
Sep 2
Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming
The paper introduces an efficient method for learning balanced signed graph Laplacians directly from data. By extending the CLIME sparse inverse covariance estimation framework, it formulates a linear programming problem for each Laplacian column with sign constraints that enforce positive edges between nodes of the same polarity and negative edges otherwise. The authors develop a tailored ADMM-based sparse LP solver, prove convergence properties, and demonstrate through experiments that the learned balanced graphs outperform existing methods and allow the reuse of spectral filtering tools, wavelets, and graph neural networks designed for positive graphs.
arXiv Machine Learning
Sep 17
Provable Guarantees for Spectral Structured Prediction
The paper presents provable guarantees for a spectral method that recovers binary node labels on signed graphs with edge‑flip noise. It provides graph‑structure‑agnostic bounds on approximate inference accuracy and maximum angle deviation, using matrix concentration and eigenvector perturbation techniques. The results connect to the Cheeger constant and are validated with synthetic experiments, marking the first theoretical analysis of this spectral approach.