Large-scale graph generation: Recent results of the SPP 1736 – Part II
| dc.contributor.author | Meyer, Ulrich | |
| dc.contributor.author | Penschuck, Manuel | |
| dc.date.accessioned | 2021-06-21T09:38:45Z | |
| dc.date.available | 2021-06-21T09:38:45Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | The 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. | en |
| dc.identifier.doi | 10.1515/itit-2019-0041 | |
| dc.identifier.pissn | 2196-7032 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/36566 | |
| dc.language.iso | en | |
| dc.publisher | De Gruyter | |
| dc.relation.ispartof | it - Information Technology: Vol. 62, No. 3-4 | |
| dc.subject | Random graphs | |
| dc.subject | scalable graph generators | |
| dc.subject | algorithm engineering | |
| dc.title | Large-scale graph generation: Recent results of the SPP 1736 – Part II | en |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 144 | |
| gi.citation.publisherPlace | Berlin | |
| gi.citation.startPage | 135 |