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Guidelines for Experimental Algorithmics: A Case Study in Network Analysis

Department of Computer Science, Humboldt-Universität zu Berlin, 10099 Berlin, Germany
Algorithms and Complexity Group, Institute of Logic and Computation, TU Wien, 1040 Vienna, Austria
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Algorithms 2019, 12(7), 127;
Received: 1 June 2019 / Revised: 19 June 2019 / Accepted: 19 June 2019 / Published: 26 June 2019
PDF [711 KB, uploaded 4 July 2019]


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. View Full-Text
Keywords: experimental algorithmics; network analysis; applied graph algorithms; statistical analysis of algorithms experimental algorithmics; network analysis; applied graph algorithms; statistical analysis of algorithms

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Angriman, E.; van der Grinten, A.; von Looz, M.; Meyerhenke, H.; Nöllenburg, M.; Predari, M.; Tzovas, C. Guidelines for Experimental Algorithmics: A Case Study in Network Analysis. Algorithms 2019, 12, 127.

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