Controllability of Social Networks and the Strategic Use of Random Information
| Autor: | Francesca Casamassima, Marco Cremonini |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Controllability
FOS: Computer and information sciences Physics - Physics and Society Theoretical computer science Computer science media_common.quotation_subject Adaptive networks FOS: Physical sciences 02 engineering and technology Physics and Society (physics.soc-ph) Recommender system Random information 01 natural sciences lcsh:QA75.5-76.95 0103 physical sciences 0202 electrical engineering electronic engineering information engineering 010306 general physics Clustering coefficient media_common Social network Structure (mathematical logic) Social and Information Networks (cs.SI) lcsh:T58.5-58.64 lcsh:Information technology business.industry Research Computer Science - Social and Information Networks Deception Influencer marketing Computer Science Applications Human-Computer Interaction Modeling and Simulation Metrics 020201 artificial intelligence & image processing lcsh:Electronic computers. Computer science business Network effect Social control Information Systems |
| Zdroj: | Computational Social Networks Computational Social Networks, Vol 4, Iss 1, Pp 1-22 (2017) |
| DOI: | 10.48550/arxiv.1807.07761 |
| Popis: | Background This work is aimed at studying realistic social control strategies for social networks based on the introduction of random information into the state of selected driver agents. Deliberately exposing selected agents to random information is a technique already experimented in recommender systems or search engines, and represents one of the few options for influencing the behavior of a social context that could be accepted as ethical, could be fully disclosed to members, and does not involve the use of force or of deception. Methods Our research is based on a model of knowledge diffusion applied to a time-varying adaptive network and considers two well-known strategies for influencing social contexts: One is the selection of few influencers for manipulating their actions in order to drive the whole network to a certain behavior; the other, instead, drives the network behavior acting on the state of a large subset of ordinary, scarcely influencing users. The two approaches have been studied in terms of network and diffusion effects. The network effect is analyzed through the changes induced on network average degree and clustering coefficient, while the diffusion effect is based on two ad hoc metrics which are defined to measure the degree of knowledge diffusion and skill level, as well as the polarization of agent interests. Results The results, obtained through simulations on synthetic networks, show a rich dynamics and strong effects on the communication structure and on the distribution of knowledge and skills. Conclusions These findings support our hypothesis that the strategic use of random information could represent a realistic approach to social network controllability, and that with both strategies, in principle, the control effect could be remarkable. |
| Databáze: | OpenAIRE |
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