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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.

Information technology industries --- Computer science --- 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

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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.

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