This paper investigates whether vectors of graph-spectral features can be used for the purposes of graph-clustering. We commence from the eigenvalues and eigenvectors of the adjacency matrix. Each of the leading eigenmodes represents a cluster of nodes and is mapped to a component of a feature vector. The spectral features used as components of the vectors are the eigenvalues, the cluster volume, the cluster perimeter, the cluster Cheeger constant, the inter-cluster edge distance, and the shared perimeter length. We explore whether these vectors can be used for the purposes of graph-clustering. Here we investigate the use of both central and pairwise clustering methods. On a data-base of view-graphs, the vectors of eigenvalues and shared perimeter lengths provide the best clusters.
CITATION STYLE
Luo, B., Wilson, R. C., & Hancock, E. R. (2002). Spectral feature vectors for graph clustering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2396, pp. 83–93). Springer Verlag. https://doi.org/10.1007/3-540-70659-3_8
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