On Eigenvalues of Laplacian Matrix for a Class of Directed Signed Graphs
Not on the map. Why?
Abstract (arXiv)
The eigenvalues of the Laplacian matrix for a class of directed graphs with both positive and negative weights are studied. First, a class of directed signed graphs is investigated in which one pair of nodes (either connected or not) is perturbed with negative weights. A necessary condition is proposed to attain the following objective for the perturbed graph: the real parts of the non-zero eigenvalues of its Laplacian matrix are positive. A sufficient condition is also presented that ensures the aforementioned objective for the unperturbed graph. It is then highlighted the case where the condition becomes necessary and sufficient. Secondly, for directed graphs, a subset of pairs of nodes are identified where if any of the pairs is connected by an edge with infinitesimal negative weight, the resulting Laplacian matrix will have at least one eigenvalue with negative real part. Illustrative examples are presented to show the applicability of our results.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 May 2017.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 0; 2025: 1.
2 papers.
- 2021Laplacian-Based Dimensionality Reduction Including Spectral Clustering, Laplacian Eigenmap, Locality Preserving Projection, Graph Embedding, and Diffusion Map: Tutorial and Surveycited by 8
- 2025A Method for Handling Negative Similarities in Explainable Graph Spectral Clustering of Text Documents -- Extended Versioncited by 1
- 2025A Method for Handling Negative Similarities in Explainable Graph Spectral Clustering of Text Documents -- Extended Version16 Apr 2025
- 2021Laplacian-Based Dimensionality Reduction Including Spectral Clustering, Laplacian Eigenmap, Locality Preserving Projection, Graph Embedding, and Diffusion Map: Tutorial and Survey3 Jun 2021