Name
Abstract | ||
|---|---|---|
Recent research in the field of complex networks has shown that - beyond microscopic structural qualities - global statistical parameters are sufficient to describe a surprising number of their macroscopic properties. This article argues that such statistical parameters can be monitored by nodes in a decentralized and efficient way. The so achieved awareness of a network's global parameters can be used by nodes for actively influencing them to optimize relevant characteristics of the overall network. For such an adaptation, the network-analogy of "phase transitions" in physical systems can be used. In this article the general concept of such an awareness-driven statistical adaptation is presented using power law networks as an example. For this important class of networks practical algorithms are introduced: Based on recent advances in reliable power law fitting, a gossip scheme has been developed which is suitable to make individual nodes aware of a power law network's critical exponent. In order to influence this parameter, decentralized reconnection rules are presented. The combination of both strategies facilitates a feedback control of large scale systems' emergent power law properties. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/SASO.2008.49 | Venezia |
Keywords | Field | DocType |
overall network,awareness-driven phase transitions,emergent power law property,reliable power law fitting,power law network,large scale,networks practical algorithm,global statistical parameter,awareness-driven statistical adaptation,decentralized reconnection rule,statistical parameter,complex network,fitting,statistical analysis,mathematical model,physics,complex networks,networks,phase transition,critical exponent,distributed systems,power law,self organization,distributed system,knowledge engineering,feedback control | Statistical parameter,Computer science,Physical system,Self-organization,Gossip,Knowledge engineering,Complex network,Critical exponent,Power law,Distributed computing | Conference |
ISBN | Citations | PageRank |
978-0-7695-3404-6 | 8 | 0.53 |
References | Authors | |
12 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ingo Scholtes | 1 | 288 | 26.66 |
| Jean Botev | 2 | 123 | 13.55 |
| Alexander Höhfeld | 3 | 28 | 3.51 |
| Hermann Schloss | 4 | 92 | 8.21 |
| Markus Esch | 5 | 156 | 12.90 |