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Graph Algorithms and Applications

The mixture of data in real-life exhibits structure or connection property in nature. Typical data include biological data, communication network data, image data, etc. Graphs provide a natural way to represent and analyze these types of data and their relationships. Unfortunately, the related algor...

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Guardat en:
Format: Online
Idioma:anglès
Publicat: MDPI - Multidisciplinary Digital Publishing Institute 2022
Matèries:
planar graphs
k-planarity
NP-hardness
polynomial time reduction
cliques
paths
computational social choice
election control
multi-winner election
social influence
influence maximization
congestion games
pure Nash equilibrium
potential games
price of anarchy
price of stability
phylogenetic tree
evolutionary tree
ancestral mixture model
mixture tree
mixture distance
tree comparison
clique independent set
clique transversal number
signed clique transversal function
minus clique transversal function
k-fold clique transversal set
distance-hereditary graphs
stretch number
recognition problem
forbidden subgraphs
hole detection
analysis and design or graph algorithms
distributed graph and network algorithms
graph theory with algorithmic applications
computational complexity of graph problems
experimental evaluation of graph algorithms
thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries
thema EDItEUR::U Computing and Information Technology::UY Computer science
Accés en línia:ONIX_20220506_9783036515427_172
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Sumari:The mixture of data in real-life exhibits structure or connection property in nature. Typical data include biological data, communication network data, image data, etc. Graphs provide a natural way to represent and analyze these types of data and their relationships. Unfortunately, the related algorithms usually suffer from high computational complexity, since some of these problems are NP-hard. Therefore, in recent years, many graph models and optimization algorithms have been proposed to achieve a better balance between efficacy and efficiency. This book contains some papers reporting recent achievements regarding graph models, algorithms, and applications to problems in the real world, with some focus on optimization and computational complexity.

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