Activity driven modeling of time varying networks
Open accessAbstract
Network modeling plays a critical role in identifying statistical regularities and structural principles common to many systems. The large majority of recent modeling approaches are connectivity driven. The structural patterns of the network are at the basis of the mechanisms ruling the network formation. Connectivity driven models necessarily provide a time-aggregated representation that may fail to describe the instantaneous and fluctuating dynamics of many networks. We address this challenge by defining the activity potential, a time invariant function characterizing the agents' interactions and constructing an activity driven model capable of encoding the instantaneous time description of the network dynamics. The model provides an explanation of structural features such as the presence of hubs, which simply originate from the heterogeneous activity of agents. Within this framework, highly dynamical networks can be described analytically, allowing a quantitative discussion of the biases induced by the time-aggregated representations in the analysis of dynamical processes.
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Informational influence theory for group polarization holds that agents with moderate opinions may become extreme while interacting in a group.
“This mechanism builds on the idea of informational influence theory [38] for the phenomenon of group polarization, where agents with moderate opinions may become extreme while interacting in a group [31].”
From Modeling echo chambers and polarization dynamics in social networks (2020) · cites this paper as [38]
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Randomized Reference Models for Temporal Networks
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- Authors
- Perra, N., Gonçalves, B., Pastor-Satorras, R., & Vespignani, A.
- Affiliations
- Northeastern University (US)
- Corresponding author
- Perra, N.
- Venue
- Scientific Reports
- Published
- 2012-06-25
- Volume
- 2
- Issue
- 1
- Pages
- 469-469
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC3384079
- OpenAlex
- W1993099985
- Cited by
- 709
- References
- 50
- Citations by year
- 2026: 36 · 2025: 43 · 2024: 40 · 2023: 50 · 2022: 79 · 2021: 66 · 2020: 44 · 2019: 65 · 2018: 80 · 2017: 46 · 2016: 44 · 2015: 49 · 2014: 37 · 2013: 23 · 2012: 7
- FWCI
- 28.08
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Scientific Reports (publishedVersion, cc-by-nc-sa)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion, cc-by-nc-sa)
- PubMed (publishedVersion)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (publishedVersion, other-oa)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (publishedVersion, cc-by-nc-nd)
- Greenwich Academic Literature Archive (University of Greenwich) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Generalitat de Catalunya
- Institució Catalana de Recerca i Estudis Avançats
- Ministerio de Ciencia e Innovación
- Army Research Laboratory (W911NF, W911NF-09-2-0053)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Physical Sciences › Physics and Astronomy › Condensed Matter Physics › Theoretical and Computational Physics
- Keywords
- dynamic networks, network dynamics, network formation, hubs, dynamical processes on networks
- MeSH terms
- Algorithms, Computer Simulation, Models, Theoretical, Time Factors, Neural Networks, Computer, Nonlinear Dynamics
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