Work

Generalized Median Graph via Iterative Alternate Minimizations

Year: 2019

Type: preprint

Abstract: Computing a graph prototype may constitute a core element for clustering or classification tasks. However, its computation is an NP-Hard problem, even for simple classes of graphs. In this paper, we propose an efficient approach based on block coordinate descent to compute a generalized median graph from a set of graphs. This approach relies on a clear definition of the optimization process and handles labeling on both edges and nodes. This iterative process optimizes the edit operations to perform on a graph alternatively on nodes and edges. Several experiments on different datasets show the efficiency of our approach.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1906.11009

Authors: Boria, Nicolas, Bougleux, S'ebastien, Gaüzère, Benoit, Brun, Luc

Institutions: Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes, GREYC

Language: English

Topic: Graph Theory and Algorithms

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Région Normandie

OpenAlex ID: W4286482082

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