Large-scale Graph Generation and Big Data: An Overview on Recent Results
Ulrich Meyer, Manuel Penschuck · Bulletin of the European Association for Theoretical Computer Science · 2017
Artificially generated input graphs play an important role in algorithm engineering for systematic testing and tuning. In big data settings, however, not only processing huge graphs but also the ecient generation of appropriate test instances itself becomes challenging. In this context we survey a number of recent results for large-scale graph generation obtained within the DFG priority programme SPP 1736 (Algorithms for Big Data).