## 2 Special Issue  
In response to the call for papers, a total of fourteen manuscripts were submitted. Out of them, we have selected six submissions to appear in the special issue at hand. All submissions have been reviewed by at least three experts of the field, and all accepted papers are thus of high quality, reflecting the growing interest in the area of algorithm engineering. In what follows, we chiefly overview the content of all published papers in increasing order of their publication dates. Paper [2] studies issues affecting the implementation, testing, and experimentation of algorithms for computational geometry problems. The focus, in particular, is on the effect of the \"quality of implementation\" on the resulting performance of an algorithm of this category. In more detail, the main contribution provided by the authors is an investigation of the efficiency of six known algorithms to compute the convex-hull problem in the two-dimensional Euclidean space, namely plane-sweep, torch, quickhull, poles-first, introhull and throw-away. The authors show that these implementations are space-efficient and time-efficient, both in theory and practice. Moreover, a test framework that can be used to make the programs computing convex hulls self-testing is introduced. Finally, general conclusions and observations on how the quality of implementation really matters in experimental studies are presented. [PERSON] (2016) deals with the bamboo garden trimming (BGT) problem, a periodic scheduling problem with several practical applications, e.g., in machine maintenance or cloud computing. The problem is known to be NP-hard due to its close relationship to another well-known optimization problem, namely, the pinwheel scheduling problem. The contributions of this paper are twofold and are both theoretical and experimental. In particular, the main focus is on understanding the behavior of natural strategies for solving the problem that are known as priority schedulings: several results are provided for this category of algorithm, ranging from stabilization properties to approximation guarantees. Furthermore, an extended experimental evaluation is conducted to support a conjecture that is presented in the paper. [PERSON] (2016) addresses methodological issues and standards in algorithmic experimentation and accordingly provides broad guidelines for experimental algorithmics. In particular, the authors focus on methodologies for the experimental part of algorithm engineering for network analysis, which is an important ingredient for a research area with an empirical nature. More precisely, for the empirical analysis of network data, existing recommendations from different fields are unified and adapted, and universal guidelines for the systematic evaluation of network analysis algorithms are proposed, including statistical analyses. This way, the behavior of newly proposed algorithms can be properly assessed, and comparisons to existing solutions become meaningful. The main technical contribution of the paper is SimexPal, a highly automated tool to perform and analyze experiments following the mentioned guidelines. To illustrate the merits of SimexPal and of the guidelines, the authors apply them to a recently proposed algorithm for approximating betweenness centrality, a prominent problem in network analysis. Both the guidelines and SimexPal can modernize and complement previous efforts in experimental algorithmics; they are not only useful for network analysis, but also in related contexts. [PERSON] (2016) investigates the problem of link recommendation via social influence, a network design problem arising in the context of election control. More specifically, political parties recently learned that they must use social media campaigns along with advertising on traditional media to defeat their opponents. Before the campaign starts, it is important for a political party to establish and ensure its media presence, e.g., by enlarging their number of connections in the social network to reach a larger portion of users.