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26 june 2019 16:02:24

 
Algorithms, Vol. 12, Pages 127: Guidelines for Experimental Algorithmics: A Case Study in Network Analysis (Algorithms)
 


The field of network science is a highly interdisciplinary area; for the empirical analysis of network data, it draws algorithmic methodologies from several research fields. Hence, research procedures and descriptions of the technical results often differ, sometimes widely. In this paper we focus on methodologies for the experimental part of algorithm engineering for network analysis—an important ingredient for a research area with empirical focus. More precisely, we unify and adapt existing recommendations from different fields and propose universal guidelines—including statistical analyses—for the systematic evaluation of network analysis algorithms. This way, the behavior of newly proposed algorithms can be properly assessed and comparisons to existing solutions become meaningful. Moreover, as the main technical contribution, we provide , a highly automated tool to perform and analyze experiments following our guidelines. To illustrate the merits of and our guidelines, we apply them in a case study: we design, perform, visualize and evaluate experiments of a recent algorithm for approximating betweenness centrality, an important problem in network analysis. In description, both our guidelines and shall modernize and complement previous efforts in experimental algorithmics; they are not only useful for network analysis, but also in related contexts.


 
121 viewsCategory: Informatics
 
Algorithms, Vol. 12, Pages 125: Correction: Sato, M., et al. Total Optimization of Energy Networks in a Smart City by Multi-Population Global-Best Modified Brain Storm Optimization with Migration, Algorithms 2019, 12, 15 (Algorithms)
Algorithms, Vol. 12, Pages 126: A New Regularized Reconstruction Algorithm Based on Compressed Sensing for the Sparse Underdetermined Problem and Applications of One-Dimensional and Two-Dimensional Signal Recovery (Algorithms)
 
 
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