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Graph Algorithms and Applications
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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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Keywords
- analysis and design or graph algorithms
- ancestral mixture model
- clique independent set
- clique transversal number
- cliques
- computational complexity of graph problems
- computational social choice
- Computer science
- Computing & information technology
- congestion games
- distance-hereditary graphs
- distributed graph and network algorithms
- Economics, finance, business & management
- election control
- evolutionary tree
- experimental evaluation of graph algorithms
- forbidden subgraphs
- graph theory with algorithmic applications
- hole detection
- Industry & industrial studies
- influence maximization
- Information technology industries
- k-fold clique transversal set
- k-planarity
- Media, information & communication industries
- minus clique transversal function
- mixture distance
- mixture tree
- multi-winner election
- NP-hardness
- paths
- phylogenetic tree
- planar graphs
- polynomial time reduction
- potential games
- price of anarchy
- price of stability
- pure Nash equilibrium
- recognition problem
- signed clique transversal function
- Social influence
- stretch number
- tree comparison
Links
DOI: 10.3390/books978-3-0365-1541-0Editions