scholarly journals On experimental algorithmics

Ubiquity ◽  
2011 ◽  
Vol 2011 (August) ◽  
pp. 1-14
Author(s):  
Richard T. Snodgrass
Keyword(s):  
Experimental Algorithmics

Experimental algorithmics

2007 ◽  
Vol 50 (11) ◽  
pp. 27-31 ◽  
Author(s):  
Catherine C. McGeoch
Keyword(s):  
Experimental Algorithmics

scholarly journals Note on generalization in experimental algorithmics

2000 ◽  
Vol 26 (4) ◽  
pp. 568-580 ◽  
Author(s):  
Naren Ramakrishnan ◽  
Raúl E. Valdés-Pérez
Keyword(s):  
Experimental Algorithmics

scholarly journals An experimental evaluation of refinement techniques for the subgraph isomorphism backtracking algorithms

2020 ◽  
Vol 11 (1) ◽  
pp. 33-42
Author(s):  
Jurij Mihelič ◽  
Uroš Čibej
Keyword(s):  
Experimental Evaluation ◽  
Isomorphism Problem ◽  
Subgraph Isomorphism ◽  
Hard Problem ◽  
Practical Algorithms ◽  
Experimental Algorithmics ◽  
Speed Up

AbstractIn this paper, we study a well-known computationally hard problem, called the subgraph isomorphism problem where the goal is for a given pattern and target graphs to determine whether the pattern is a subgraph of the target graph. Numerous algorithms for solving the problem exist in the literature and most of them are based on the backtracking approach. Since straightforward backtracking is usually slow, many algorithmic refinement techniques are used in practical algorithms. The main goal of this paper is to study such refinement techniques and to determine their ability to speed up backtracking algorithms. To do this we use a methodology of experimental algorithmics. We perform an experimental evaluation of the techniques and their combinations and, hence, demonstrate their usefulness in practice.

Large-scale graph generation: Recent results of the SPP 1736 – Part II

2020 ◽  
Vol 62 (3-4) ◽  
pp. 135-144
Author(s):  
Ulrich Meyer ◽  
Manuel Penschuck
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Algorithm Engineering ◽  
Data Sets ◽  
Previous Survey ◽  
Experimental Algorithmics ◽  
Legal Challenges ◽  
Algorithmic Approaches ◽  
Selection Of ◽  
Graph Generation

AbstractThe selection of input data is a crucial step in virtually every empirical study. Experimental campaigns in algorithm engineering, experimental algorithmics, network analysis, and many other fields often require suited network data. In this context, synthetic graphs play an important role, as data sets of observed networks are typically scarce, biased, not sufficiently understood, and may pose logistic and legal challenges. Just like processing huge graphs becomes challenging in the big data setting, new algorithmic approaches are necessary to generate such massive instances efficiently. Here, we update our previous survey [35] on results for large-scale graph generation obtained within the DFG priority programme SPP 1736 (Algorithms for Big Data); to this end, we broaden the scope and include recently published results.

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