Clustering Vertex-Weighted Graphs by Spectral Methods

Spectral techniques are often used to partition the set of vertices of a graph, or to form clusters. They are based on the Laplacian matrix. These techniques allow easily to integrate weights on the edges. In this work, we introduce a p-Laplacian, or a generalized Laplacian matrix with potential, wh...

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Bibliographic Details
Published inMathematics (Basel) Vol. 9; no. 22; p. 2841
Main Authors García-Zapata, Juan-Luis, Grácio, Clara
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2021
Subjects
Apexes
Clustering
Eigenvalues
Graph theory
Graphs
Laplacian graph
Mathematics
partitioning
Spectral methods
vertex-weighted graph
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Summary:Spectral techniques are often used to partition the set of vertices of a graph, or to form clusters. They are based on the Laplacian matrix. These techniques allow easily to integrate weights on the edges. In this work, we introduce a p-Laplacian, or a generalized Laplacian matrix with potential, which also allows us to take into account weights on the vertices. These vertex weights are independent of the edge weights. In this way, we can cluster with the importance of vertices, assigning more weight to some vertices than to others, not considering only the number of vertices. We also provide some bounds, similar to those of Chegeer, for the value of the minimal cut cost with weights at the vertices, as a function of the first non-zero eigenvalue of the p-Laplacian (an analog of the Fiedler eigenvalue).
ISSN:2227-7390
2227-7390
DOI:10.3390/math9222841