Detecting Stubborn Behaviors in Influence Networks : A Model-Based Approach for Resilient Analysis
(2024) In IEEE Control Systems Letters 8. p.2343-2348- Abstract
The wide spread of on-line social networks poses new challenges in information environment and cybersecurity. A key issue is detecting stubborn behaviors to identify leaders and influencers for marketing purposes, or extremists and automatic bots as potential threats. Existing literature typically relies on known network topology and extensive centrality measures computation. However, the size of social networks and their often unknown structure could make social influence computation impractical. We propose a new approach based on opinion dynamics to estimate stubborn agents from data. We consider a DeGroot model in which regular agents adjust their opinions as a linear combination of their neighbors' opinions, whereas stubborn agents... (More)
The wide spread of on-line social networks poses new challenges in information environment and cybersecurity. A key issue is detecting stubborn behaviors to identify leaders and influencers for marketing purposes, or extremists and automatic bots as potential threats. Existing literature typically relies on known network topology and extensive centrality measures computation. However, the size of social networks and their often unknown structure could make social influence computation impractical. We propose a new approach based on opinion dynamics to estimate stubborn agents from data. We consider a DeGroot model in which regular agents adjust their opinions as a linear combination of their neighbors' opinions, whereas stubborn agents keep their opinions constant over time. We formulate the stubborn nodes identification and their influence estimation problems as a low-rank approximation problem. We then propose an interpolative decomposition algorithm for their solution. We determine sufficient conditions on the model parameters to ensure the algorithm's resilience to noisy observations. Finally, we corroborate our theoretical analysis through numerical results.
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- author
- Raineri, Roberta ; Ravazzi, Chiara ; Como, Giacomo LU and Fagnani, Fabio
- organization
- publishing date
- 2024
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- detection, identification, Network analysis and control, social networks
- in
- IEEE Control Systems Letters
- volume
- 8
- pages
- 6 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85205761984
- ISSN
- 2475-1456
- DOI
- 10.1109/LCSYS.2024.3472495
- language
- English
- LU publication?
- yes
- id
- 0cec94df-18f5-4ade-a098-73638f0b63eb
- date added to LUP
- 2025-01-02 13:24:29
- date last changed
- 2025-04-04 14:02:00
@article{0cec94df-18f5-4ade-a098-73638f0b63eb,
abstract = {{<p>The wide spread of on-line social networks poses new challenges in information environment and cybersecurity. A key issue is detecting stubborn behaviors to identify leaders and influencers for marketing purposes, or extremists and automatic bots as potential threats. Existing literature typically relies on known network topology and extensive centrality measures computation. However, the size of social networks and their often unknown structure could make social influence computation impractical. We propose a new approach based on opinion dynamics to estimate stubborn agents from data. We consider a DeGroot model in which regular agents adjust their opinions as a linear combination of their neighbors' opinions, whereas stubborn agents keep their opinions constant over time. We formulate the stubborn nodes identification and their influence estimation problems as a low-rank approximation problem. We then propose an interpolative decomposition algorithm for their solution. We determine sufficient conditions on the model parameters to ensure the algorithm's resilience to noisy observations. Finally, we corroborate our theoretical analysis through numerical results.</p>}},
author = {{Raineri, Roberta and Ravazzi, Chiara and Como, Giacomo and Fagnani, Fabio}},
issn = {{2475-1456}},
keywords = {{detection; identification; Network analysis and control; social networks}},
language = {{eng}},
pages = {{2343--2348}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Control Systems Letters}},
title = {{Detecting Stubborn Behaviors in Influence Networks : A Model-Based Approach for Resilient Analysis}},
url = {{http://dx.doi.org/10.1109/LCSYS.2024.3472495}},
doi = {{10.1109/LCSYS.2024.3472495}},
volume = {{8}},
year = {{2024}},
}