Handbook of Large-Scale Random Networks

• Corporate Author: SpringerLink (Online service)
• Other Authors: Bollobás, Béla (Editor), Kozma, Robert (Editor), Miklós, Dezső (Editor)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.
• Series: Bolyai Society Mathematical Studies, 18
• Subjects: Mathematics.; Computers.; Computer science > Mathematics.; Computer simulation.; Mathematical models.; Discrete mathematics.; Combinatorics.; Discrete Mathematics.; Mathematical Modeling and Industrial Mathematics.; Theory of Computation.; Simulation and Modeling.; Discrete Mathematics in Computer Science.

 This handbook describes advances in large scale network studies that have taken place in the past 5 years since the publication of the Handbook of Graphs and Networks in 2003. It covers all aspects of large-scale networks, including mathematical foundations and rigorous results of random graph theory, modeling and computational aspects of large-scale networks, as well as areas in physics, biology, neuroscience, sociology and technical areas. Applications range from microscopic to mesoscopic and macroscopic models. The book is based on the material of the NSF workshop on Large-scale Random Graphs held in Budapest in 2006, at the Alfréd Rényi Institute of Mathematics, organized jointly with the University of Memphis. 

 Random Graphs and Branching Processes -- Percolation, Connectivity, Coverage and Colouring of Random Geometric Graphs -- Scaling Properties of Complex Networks and Spanning Trees -- Random Tree Growth with Branching Processes — A Survey -- Reaction-diffusion Processes in Scale-free Networks -- Toward Understanding the Structure and Function of Cellular Interaction Networks -- Scale-Free Cortical Planar Networks -- Reconstructing Cortical Networks: Case of Directed Graphs with High Level of Reciprocity -- k-Clique Percolation and Clustering -- The Inverse Problem of Evolving Networks — with Application to Social Nets -- Learning and Representation: From Compressive Sampling to the ‘Symbol Learning Problem’ -- Telephone Call Network Data Mining: A Survey with Experiments.