A new dissimilarity measure for comparing labeled graphs
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TLDR
The special cases of the proposed general framework using eigendecomposition of graph Laplacians, a new dissimilarity measure that avoids problems of spectral analysis, are shown.About:
This article is published in Linear Algebra and its Applications.The article was published on 2013-03-01 and is currently open access. It has received 10 citations till now. The article focuses on the topics: Line graph & Graph property.read more
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Topology-varying 3D shape creation via structural blending
TL;DR: An algorithm for generating novel 3D models via topology-varying shape blending via continuous structural blending between man-made shapes exhibiting complex topological differences, in real time is introduced.
Journal ArticleDOI
Fast computation of von Neumann entropy for large-scale graphs via quadratic approximations
TL;DR: This work proposes novel quadratic approximations for fast computing von Neumann graph entropy and reduces the cubic complexity of VNGE to linear complexity.
Journal ArticleDOI
Comparing large-scale graphs based on quantum probability theory
TL;DR: It is shown that the spectral distributions of their adjacency matrices in a vector state includes information not only about their eigenvalues, but also about the corresponding eigenvectors of their spectral distributions.
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Selecting Graph Cut Solutions via Global Graph Similarity
TL;DR: This work shows the problem in similarity graph-based clustering setting that the resulting clusters might be even disconnected, and derives a graph similarity measure that shows high similarity values to the original graph for good clustering solutions.
Posted Content
Fast computation of von Neumann entropy for large-scale graphs via quadratic approximations
TL;DR: In this article, the von Neumann graph entropy (VNGE) is used as a measure of graph complexity, which can be the measure of information divergence and distance between graphs.
References
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Journal Article
Binary codes capable of correcting deletions, insertions, and reversals
Book
Spectral Graph Theory
TL;DR: Eigenvalues and the Laplacian of a graph Isoperimetric problems Diameters and eigenvalues Paths, flows, and routing Eigen values and quasi-randomness
Proceedings ArticleDOI
Clustering with Bregman Divergences
TL;DR: This paper proposes and analyzes parametric hard and soft clustering algorithms based on a large class of distortion functions known as Bregman divergences, and shows that there is a bijection between regular exponential families and a largeclass of BRegman diverGences, that is called regular Breg man divergence.
Graph Kernels
TL;DR: A unified framework to study graph kernels is presented and a kernel that is close to the optimal assignment kernel of kernel of Frohlich et al. (2006) yet provably positive semi-definite is provided.