Spectral Simplification of Graphs

Qiu, Huaijun; Hancock, Edwin R.

doi:10.1007/978-3-540-24673-2_10

Cited by 14 publications

(12 citation statements)

References 14 publications

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“…The results are compared with those obtained using the method of Luo and Hancock [12] and the partition matching method of Qiu and Hancock [20] in Table 1. This table contains the number of detected corners to be matched, the number of correct correspondences, the number of missed corners and the number of miss-matched corners.…”

Section: Multilayer Graph Matchingmentioning

confidence: 99%

Graph simplification and matching using commute times

Qiu

Hancock

2007

Pattern Recognition

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Section: Multilayer Graph Matchingmentioning

confidence: 99%

Graph simplification and matching using commute times

Qiu

Hancock

2007

Pattern Recognition

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“…The Laplacian [10] of the graph is defined by L = D − A, where D is the diagonal node degree matrix whose elements D ii = n k=1 A ik . The Laplacian matrix of G is positive semidefinite and singular, and it is more often adopted for spectral analysis than the adjacency matrix because of its properties.…”

Section: Previous Eigendecomposition Approaches For Graph Comparisonmentioning

confidence: 99%

An Inexact Graph Comparison Approach in Joint Eigenspace

Lee

Duin

2008

Lecture Notes in Computer Science

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Abstract. In graph comparison, the use of (dis)similarity measurements between graphs is an important topic. In this work, we propose an eigendecomposition based approach for measuring dissimilarities between graphs in the joint eigenspace (JoEig). We will compare our JoEig approach with two other eigendecomposition based methods that compare graphs in different eigenspaces. To calculate the dissimilarity between graphs of different sizes and perform inexact graph comparison, we further develop three different ways to resize the eigenspectra and study their performance in different situations.

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“…Qiu and Hancock [14,15] develop three graph simplification algorithms. The first algorithm decomposes a large graph into smaller non-overlapping sub-graphs.…”

Section: Related Workmentioning

confidence: 99%