
This paper introduces a new class of games called symmetric complementing games. These games are interesting since their related complexity classes include many well-known graph problems: Finding minimum spanning forests; k-connectivity and k-blocks; and recognition of chordal graphs, comparability graphs, interval graphs, split graphs, permutation graphs, and constant valence planar graphs. For these problems probabilistic sequential algorithms requiring simultaneously logarithmic space and polynomial time are given. Furthermore, probabilistic parallelism algorithms requiring simultaneously logarithmic time and a polynomial number of processors are also given.
polynomial time, Graph theory (including graph drawing) in computer science, Analysis of algorithms and problem complexity, Applications of game theory, polynomial number of processors, graph problems, probabilistic sequential algorithms, logarithmic space, symmetric complementing games, logarithmic time, complexity classes, probabilistic parallelism algorithms
polynomial time, Graph theory (including graph drawing) in computer science, Analysis of algorithms and problem complexity, Applications of game theory, polynomial number of processors, graph problems, probabilistic sequential algorithms, logarithmic space, symmetric complementing games, logarithmic time, complexity classes, probabilistic parallelism algorithms
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 76 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 1% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
