Handbook of large-scale random networks
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 theor...
Gespeichert in:
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| Weitere Verfasser: | , |
| Format: | Livre numérique |
| Sprache: | Anglais |
| Veröffentlicht: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Schriftenreihe: | Bolyai Society Mathematical Studies
18 |
| Schlagworte: | |
| Online Zugang: | Accès sur la plateforme de l'éditeur Accès sur la plateforme de l'éditeur (Springer) Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Anmerkung: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Handbook of Large-Scale Random Networks, Texte imprimé, 9783540865506 • Handbook of Large-Scale Random Networks, Texte imprimé, 9783642242298 • Handbook of large-scale random networks, Béla Bollobás, Robert Kozma, Dezső Miklós, eds., 2009, Berlin, Springer Verlag, Janos Bolyai Mathematical Society, 1 vol. (538 p.), Bolyai Society mathematical studies, 978-3-540-69394-9 |
Inhaltsangabe:
- 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.