SPREAD OF BELIEFS
The spread of Information on real-world networks
| Coordinatore | MAX PLANCK GESELLSCHAFT ZUR FOERDERUNG DER WISSENSCHAFTEN E.V.
Organization address
address: Hofgartenstrasse 8 contact info |
| Nazionalità Coordinatore | Germany [DE] |
| Totale costo | 155˙461 € |
| EC contributo | 155˙461 € |
| Programma | FP7-PEOPLE
Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) |
| Code Call | FP7-PEOPLE-2009-IEF |
| Funding Scheme | MC-IEF |
| Anno di inizio | 2010 |
| Periodo (anno-mese-giorno) | 2010-11-01 - 2011-03-31 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
MAX PLANCK GESELLSCHAFT ZUR FOERDERUNG DER WISSENSCHAFTEN E.V.
Organization address
address: Hofgartenstrasse 8 contact info |
DE (MUENCHEN) | coordinator | 155˙461.00 |
Mappa
Word cloud
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Obiettivo del progetto (Objective)
'The main theme of this project is the rigorous analysis of mathematical models for the dissemination of information on networks whose characteristics approximate those of networks that emerge in social life as well as in nature. In particular, we intend to study mathematical models which describe the spread of new beliefs and ideas within a given network. Such models have become of great importance in the last decade mainly due to the development of the Internet and its widespread influence on many aspects of social and economic life. Various beliefs and ideas as well as reputations of new products are spread through a word of mouth mechanism. The individuals of a certain society are influenced by those individuals they are related to and according to their own criteria they decide to adopt a new belief. On the other hand, the networks that arise in social life are not arbitrary, but exhibit certain structural characteristics which are mainly the result of their intrinsic randomness. Our primary aim is to analyse rigorously mechanisms that describe the word of mouth dissemination of information on a typical instance of a random network whose distributional characteristics are similar to those observed in real social networks. Furthermore, we intend to develop strategies that aim at the maximisation of the spread of a new belief on a typical instance of such a random network. More specifically, we will attempt to identify those structures that typically arise in a random network and facilitate the efficient spread of a new idea in it. This will lead to the design of efficient algorithms that will be working well in practice, that is, for the majority of the instances of a social network.'
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