  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.
