Models of information diffusion in social networks
| Autor: | Eric Gaussier |
|---|---|
| Přispěvatelé: | Analyse de données, Modélisation et Apprentissage automatique [Grenoble] (AMA), Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Thang Huynh Quyet, Dinh Khang Tran |
| Rok vydání: | 2011 |
| Předmět: |
Diffusion (acoustics)
I.6.5 Model Development (primary) H.3.4 & H.5.3 (additional) Percolation (cognitive psychology) Relation (database) Computer science business.industry media_common.quotation_subject 02 engineering and technology Maximization Linear threshold Machine learning computer.software_genre Data science [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Presentation [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] 020204 information systems 0202 electrical engineering electronic engineering information engineering Artificial intelligence business computer media_common |
| Zdroj: | SoICT SoICT '11-Proceedings of the Second Symposium on Information and Communication Technology SoICT 2011-Symposium on Information and Communication Technology SoICT 2011-Symposium on Information and Communication Technology, Oct 2011, Hanoi, Vietnam. pp.2-2, ⟨10.1145/2069216.2069218⟩ |
| DOI: | 10.1145/2069216.2069218 |
| Popis: | ISBN 978-1-4503-0880-9; International audience; Social networks now play a central role for sharing information and discussing different types of events. The way information spreads in such networks has often been compared to the way innovations spread in marketing or viruses spread in populations. As such, two of the more popular information diffusion models, the IC (Independent Cascade) and the LT (Linear Threshold) models, can be seen as instances of the standard SI (Susceptible-Infectious) family used in epidemiology. However, such models usually fail to account for important characteristics of the users sharing and diffusing information in social networks, namely the interest of the users in the information being disseminated and their willingness to diffuse a piece of information. After a presentation of the standard information diffusion models, we will introduce a new generation of models, referred to as "user-centric", which provide a more realistic modeling of how information spreads in social networks. We will furthermore explicit the differences between all these models along several dimensions (relation to percolation, influence maximization problem) and will illustrate the behavior of these models on both synthetic and real datasets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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