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Shape Simplification Through Graph Sparsificationle of graph sparsification is to retain only the edges which are key to the preservation of desired properties. In this regard, sparsification by edge resistance allows us to preserve (to some extent) links between protrusions and the remainder of the shape (e.g. parts of a shape) while removing in-

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Learning Graph Matching with a Graph-Based Perceptron in a Classification Contextccurate approximations have led to significant progress in a wide range of applications. Learning graph matching functions from observed data, however, still remains a challenging issue. This paper presents an effective scheme to parametrize a graph model for object matching in a classification cont

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A Nested Alignment Graph Kernel Through the Dynamic Time Warping Frameworkfically, for a pair of graphs, we commence by computing the depth-based complexity traces rooted at the centroid vertices. The resulting kernel for the graphs is defined by measuring the global alignment kernel, which is developed through the dynamic time warping framework, between the complexity tr

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Detecting Alzheimer’s Disease Using Directed Graphss. However, the structure of the directed networks representing the activation patterns, and their differences in healthy and Alzheimer’s people remain poorly understood. In this paper, we aim to identify the differences in fMRI activation network structure for patients with AD, late mild cognitive

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Error-Tolerant Coarse-to-Fine Matching Model for Hierarchical Graphs database of graphs implies a high computational complexity. Moreover, these representations are very sensitive to noise or small changes. In this work, a novel hierarchical graph representation is designed. Using graph clustering techniques adapted from graph-based social media analysis, we propose