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E. Angriman, A. van der Grinten, M. von Looz, H. Meyerhenke, M. Nöllenburg, M. Predari, C. Tzovas:
"Guidelines for Experimental Algorithmics: A Case Study in Network Analysis";
Algorithms, Volume 12 (2019), Issue 7; S. 1 - 37.

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 SimexPal, a highly automated tool to perform and analyze experiments following our
guidelines. To illustrate the merits of SimexPal 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 summary, both our guidelines
and SimexPal shall modernize and complement previous efforts in experimental algorithmics; they are
not only useful for network analysis, but also in related contexts.

http://dx.doi.org/10.3390/a12070127

https://publik.tuwien.ac.at/files/publik_280640.pdf