6 research outputs found
Controllability of Social Networks and the Strategic Use of Random Information
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.
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 defined to measure the degree of knowledge diffusion and skill level,
as well as the polarization of agent interests. 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,
supporting 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
Low-dimensional controllability of brain networks
Network controllability is a powerful tool to study causal relationships in
complex systems and identify the driver nodes for steering the network dynamics
into desired states. However, due to ill-posed conditions, results become
unreliable when the number of drivers becomes too small compared to the network
size. This is a very common situation, particularly in real-world applications,
where the possibility to access multiple nodes at the same time is limited by
technological constraints, such as in the human brain. Although targeting
smaller network parts might improve accuracy, challenges may remain for
extremely unbalanced situations, when for example there is one single driver.
To address this problem, we developed a mathematical framework that combines
concepts from spectral graph theory and modern network science. Instead of
controlling the original network dynamics, we aimed to control its
low-dimensional embedding into the topological space derived from the network
Laplacian. By performing extensive simulations on synthetic networks, we showed
that a relatively low number of projected components is enough to improve the
overall control accuracy, notably when dealing with very few drivers. Based on
these findings, we introduced alternative low-dimensional controllability
metrics and used them to identify the main driver areas of the human connectome
obtained from N=6134 healthy individuals in the UK-biobank cohort. Results
revealed previously unappreciated influential regions compared to standard
approaches, enabled to draw control maps between distinct specialized
large-scale brain systems, and yielded an anatomically-based understanding of
hemispheric functional lateralization. Taken together, our results offered a
theoretically-grounded solution to deal with network controllability in
real-life applications and provided insights into the causal interactions of
the human brain
Integrating media content analysis, reception analysis, and media effects studies
Every day, the world of media is at our fingertips, whether it is watching movies, listening to the radio, or browsing online media. On average, people spend over 8 h per day consuming messages from the mass media, amounting to a total lifetime dose of more than 20 years in which conceptual content stimulates our brains. Effects from this flood of information range from short-term attention bursts (e.g., by breaking news features or viral ‘memes’) to life-long memories (e.g., of one’s favorite childhood movie), and from micro-level impacts on an individual’s memory, attitudes, and behaviors to macro-level effects on nations or generations. The modern study of media’s influence on society dates back to the 1940s. This body of mass communication scholarship has largely asked, “what is media’s effect on the individual?” Around the time of the cognitive revolution, media psychologists began to ask, “what cognitive processes are involved in media processing?” More recently, neuroimaging researchers started using real-life media as stimuli to examine perception and cognition under more natural conditions. Such research asks: “what can media tell us about brain function?” With some exceptions, these bodies of scholarship often talk past each other. An integration offers new insights into the neurocognitive mechanisms through which media affect single individuals and entire audiences. However, this endeavor faces the same challenges as all interdisciplinary approaches: Researchers with different backgrounds have different levels of expertise, goals, and foci. For instance, neuroimaging researchers label media stimuli as “naturalistic” although they are in many ways rather artificial. Similarly, media experts are typically unfamiliar with the brain. Neither media creators nor neuroscientifically oriented researchers approach media effects from a social scientific perspective, which is the domain of yet another species. In this article, we provide an overview of approaches and traditions to studying media, and we review the emerging literature that aims to connect these streams. We introduce an organizing scheme that connects the causal paths from media content → brain responses → media effects and discuss network control theory as a promising framework to integrate media content, reception, and effects analyses