2016/05/13 by Sébastien Gadat, Gadat, Sébastien, Ioana Gavra +3 · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #Complex Network Analysis Techniques #Data Management and Algorithms #FOS: Mathematics #Optimization and Control (math.OC) #Probability (math.PR) #Statistics Theory (math.ST) #Topological and Geometric Data Analysis #math.OC #math.PR #math.ST #stat.TH
paper · pdf · doi:10.48550/arxiv.1605.04148
5 figures, 2 tables
arxiv created 2016/05/13 · openalex publication_date 2016/05/13 · arxiv updated 2016/05/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Discrete structures like graphs make it possible to naturally and flexibly model complex phenomena. Since graphs that represent various types of information are increasingly available today, their analysis has become a popular subject of research. The graphs studied in the field of data science at this time generally have a large number of nodes that are not fairly weighted and connected to each other, translating a structural specification of the data. Yet, even an algorithm for locating the average position in graphs is lacking although this knowledge would be of primary interest for statistical or representation problems. In this work, we develop a stochastic algorithm for finding the Frechet mean of weighted undirected metric graphs. This method relies on a noisy simulated annealing algorithm dealt with using homogenization. We then illustrate our algorithm with two examples (subgraphs of a social network and of a collaboration and citation network).