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Studying network dynamics in digital ecosystems

Published: 27 October 2009 Publication History

Abstract

One of the main fields in which the research on Digital Ecosystems has been fruitfully applied is the networking field, with the aim of discovering the dynamics of relationships among the entities of the ecosystems. Following this research direction, this paper addresses the problem of predicting social dynamics of a network in order to emphasize the relationships and the potentials for collaboration and transmission of knowledge, as well as the nature and intensity of the inner sub-networks. To do this, a multi-layer Hidden Markov Model has been applied, which allows predicting the evolution of the interests and intensity of the overall network, based on the most probable evolution of each sub-network (e.g., if it increases, decreases, appears, etc.). This model has been tested using data from a large, realistic network and the prediction accuracy rate has been evaluated.

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Cited By

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  • (2018)MONDE: a method for predicting social network dynamics and evolutionEvolving Systems10.1007/s12530-018-9242-zOnline publication date: 11-Jun-2018
  • (2016)Sentiment analysis from textual to multimodal features in digital environmentsProceedings of the 8th International Conference on Management of Digital EcoSystems10.1145/3012071.3012089(137-144)Online publication date: 1-Nov-2016
  • (2014)An Ecosystemic Environment for Knowledge and Services Sharing on Creative EnterprisesProceedings of the 6th International Conference on Management of Emergent Digital EcoSystems10.1145/2668260.2668308(27-33)Online publication date: 15-Sep-2014
  • Show More Cited By

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cover image ACM Other conferences
MEDES '09: Proceedings of the International Conference on Management of Emergent Digital EcoSystems
October 2009
525 pages
ISBN:9781605588292
DOI:10.1145/1643823
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 27 October 2009

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Author Tags

  1. digital ecosystems
  2. hidden Markov models
  3. network evolution
  4. social networks

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Cited By

View all
  • (2018)MONDE: a method for predicting social network dynamics and evolutionEvolving Systems10.1007/s12530-018-9242-zOnline publication date: 11-Jun-2018
  • (2016)Sentiment analysis from textual to multimodal features in digital environmentsProceedings of the 8th International Conference on Management of Digital EcoSystems10.1145/3012071.3012089(137-144)Online publication date: 1-Nov-2016
  • (2014)An Ecosystemic Environment for Knowledge and Services Sharing on Creative EnterprisesProceedings of the 6th International Conference on Management of Emergent Digital EcoSystems10.1145/2668260.2668308(27-33)Online publication date: 15-Sep-2014
  • (2010)A two-layer ecosystem evolution modelProceedings of the International Conference on Management of Emergent Digital EcoSystems10.1145/1936254.1936269(81-86)Online publication date: 26-Oct-2010
  • (2010)A Mobile Digital Ecosystem FrameworkProceedings of the 2010 13th International Conference on Network-Based Information Systems10.1109/NBiS.2010.26(516-520)Online publication date: 14-Sep-2010

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