scholarly journals Response Inhibition in Adolescents is Moderated by Brain Connectivity and Social Network Structure

2018 ◽  
Author(s):  
Steven H. Tompson ◽  
Emily B. Falk ◽  
Matthew Brook O’Donnell ◽  
Christopher N. Cascio ◽  
Joseph B. Bayer ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Context ◽  
Response Inhibition ◽  
Brain Activity ◽  
Real Life ◽  
Processing System ◽  
Developmental Trajectory ◽  
Self Control ◽  
Social Brain

AbstractSelf-control is vital for a wide range of outcomes across our lifespan, yet the developmental trajectory of its core components during adolescence remains elusive. Many adolescents can successfully regulate their behavior even when they do not show strong activation in brain regions typically recruited during self-control in adults. Thus, adolescents may rely on other neural and cognitive resources to compensate, including daily experiences navigating and managing complex social relationships that likely bolster self-control processes. Here, we tested whether activity and connectivity in brain systems associated with social cognition (i.e., self-processing and mentalizing) facilitated successful self-control. We measured brain activity using fMRI as 62 adolescents completed a Go/No-Go response inhibition task. Recruitment of social brain systems, especially the self-processing system, was associated with better response inhibition in adolescents. Interestingly, the reliance on the self-processing system was stronger in adolescents with weaker activation in the canonical response inhibition system, suggesting a compensatory role for social brain systems during adolescent development. Furthermore, we examined the importance of social context by computing the size, number of communities, and modularity of our participants’ real-life social network. We found that adolescents with more friends and more communities in their social networks demonstrated a stronger relationship between response inhibition and recruitment of social brain systems. Collectively, our results identify the importance of social context and its moderating role on the relationship between brain activity and behavior. Furthermore, our results indicate a critical role for social brain systems during the developmental trajectory of self-control throughout adolescence.Significance StatementWe employed a network neuroscience approach to investigate the role of social context and social brain systems in facilitating self-control in adolescents. We found that recruitment of social brain systems was associated with better response inhibition in adolescents, especially for adolescents with weaker activation in the response inhibition system. Moreover, adolescents with more friends and communities in their social networks showed stronger relationships between response inhibition and recruitment of social brain systems. Our results advance understanding of how brain systems facilitate self-control in adolescents, and how these brain responses are associated with features of an adolescent’s real-life social network. Bringing together findings related to brain networks and social networks provides key insights into how biology and environment mutually influence development.

scholarly journals Response Inhibition in Adolescents is Moderated by Brain Connectivity and Social Network Structure

2018 ◽  
Author(s):  
Steven Tompson ◽  
Emily B. Falk ◽  
Matthew Brook O'Donnell ◽  
Christopher N. Cascio ◽  
Joseph Bayer ◽  
...  
Keyword(s):  
Social Network ◽  
Social Context ◽  
Response Inhibition ◽  
Brain Activity ◽  
Processing System ◽  
Developmental Trajectory ◽  
The Self ◽  
Self Control ◽  
Social Brain ◽  
Wide Range

Self-control is vital for a wide range of outcomes across our lifespan, yet the developmental trajectory of its core components during adolescence remains elusive. Many adolescents can successfully regulate their behavior even when they do not show strong activation in brain regions typically recruited during self-control in adults. Thus, adolescents may rely on other neural and cognitive resources to compensate, including daily experiences navigating and managing complex social relationships that likely bolster self-control processes. Here, we tested whether activity and connectivity in brain systems associated with social cognition (i.e., self-processing and mentalizing) facilitated successful self-control. We measured brain activity using fMRI as 62 adolescents completed a Go/No-Go response inhibition task. Recruitment of social brain systems, especially the self-processing system, was associated with better response inhibition in adolescents. Interestingly, the reliance on the self-processing system was stronger in adolescents with weaker activation in the canonical response inhibition system, suggesting a compensatory role for social brain systems during adolescent development. Furthermore, we examined the importance of social context by computing the size, number of communities, and modularity of our participants’ real-life social network. We found that adolescents with more friends and more communities in their social networks demonstrated a stronger relationship between response inhibition and recruitment of social brain systems. Collectively, our results identify the importance of social context and its moderating role on the relationship between brain activity and behavior. Furthermore, our results indicate a critical role for social brain systems during the developmental trajectory of self-control throughout adolescence.

scholarly journals The switching mechanisms of social network densification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Teruyoshi Kobayashi ◽  
Mathieu Génois
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Context ◽  
Social Interactions ◽  
Regime Switching ◽  
Face To Face ◽  
The Social ◽  
Switching Property ◽  
Distinctive Type ◽  
Insight Into

AbstractDensification and sparsification of social networks are attributed to two fundamental mechanisms: a change in the population in the system, and/or a change in the chances that people in the system are connected. In theory, each of these mechanisms generates a distinctive type of densification scaling, but in reality both types are generally mixed. Here, we develop a Bayesian statistical method to identify the extent to which each of these mechanisms is at play at a given point in time, taking the mixed densification scaling as input. We apply the method to networks of face-to-face interactions of individuals and reveal that the main mechanism that causes densification and sparsification occasionally switches, the frequency of which depending on the social context. The proposed method uncovers an inherent regime-switching property of network dynamics, which will provide a new insight into the mechanics behind evolving social interactions.

scholarly journals Understanding attribute and social circle correlation in social networks

2019 ◽  
pp. 1228-1242
Author(s):  
PRANAV NERURKAR ◽  
MADHAV CHANDANE ◽  
SUNIL BHIRUD
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Network ◽  
Real Life ◽  
Coarse Grained ◽  
Unified Framework ◽  
Community Discovery ◽  
Social Circle ◽  
The Social ◽  
Circle Formation

Social circles, groups, lists, etc. are functionalities that allow users of online social network (OSN) platforms to manually organize their social media contacts. However, this facility provided by OSNs has not received appreciation from users due to the tedious nature of the task of organizing the ones that are only contacted periodically. In view of the numerous benefits of this functionality, it may be advantageous to investigate measures that lead to enhancements in its efficacy by allowing for automatic creation of customized groups of users (social circles, groups, lists, etc). The field of study for this purpose, i.e. creating coarse-grained descriptions from data, consists of two families of techniques, community discovery and clustering. These approaches are infeasible for the purpose of automation of social circle creation as they fail on social networks. A reason for this failure could be lack of knowledge of the global structure of the social network or the sparsity that exists in data from social networking websites. As individuals do in real life, OSN clients dependably attempt to broaden their groups of contacts in order to fulfill different social demands. This means that ‘homophily’ would exist among OSN users and prove useful in the task of social circle detection. Based on this intuition, the current inquiry is focused on understanding ‘homophily’ and its role in the process of social circle formation. Extensive experiments are performed on egocentric networks (ego is user, alters are friends) extracted from prominent OSNs like Facebook, Twitter, and Google+. The results of these experiments are used to propose a unified framework: feature extraction for social circles discovery (FESC). FESC detects social circles by jointly modeling ego-net topology and attributes of alters. The performance of FESC is compared with standard benchmark frameworks using metrics like edit distance, modularity, and running time to highlight its efficacy.

Classifying the Influential Individuals in Multi-Layer Social Networks

2019 ◽  
Vol 8 (1) ◽  
pp. 21-32
Author(s):  
Ruchi Mittal ◽  
M.P.S Bhatia
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Diffusion ◽  
Real Life ◽  
Single Layer ◽  
Machine Learning Techniques ◽  
Centrality Measures ◽  
Learning Techniques ◽  
Modes Of Interaction ◽  
Source Of Information

Nowadays, social media is one of the popular modes of interaction and information diffusion. It is commonly found that the main source of information diffusion is done by some entities and such entities are also called as influencers. An influencer is an entity or individual who has the ability to influence others because of his/her relationship or connection with his/her audience. In this article, we propose a methodology to classify influencers from multi-layer social networks. A multi-layer social network is the same as a single layer social network depict that it includes multiple properties of a node and modeled them into multiple layers. The proposed methodology is a fusion of machine learning techniques (SVM, neural networks and so on) with centrality measures. We demonstrate the proposed algorithm on some real-life networks to validate the effectiveness of the approach in multi-layer systems.

scholarly journals Electrophysiological Indices of Response Inhibition in a Go/NoGo Task Predict Self-Control in a Social Context

PLoS ONE ◽  
2013 ◽  
Vol 8 (11) ◽  
pp. e79462 ◽  
Author(s):  
Kyle Nash ◽  
Bastian Schiller ◽  
Lorena R. R. Gianotti ◽  
Thomas Baumgartner ◽  
Daria Knoch
Keyword(s):  
Social Context ◽  
Response Inhibition ◽  
Self Control

A novel genetic algorithm-based improvement model for online communities and trust networks

2021 ◽  
Vol 40 (1) ◽  
pp. 1597-1608
Author(s):  
Ilker Bekmezci ◽  
Murat Ermis ◽  
Egemen Berki Cimen
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Social Network ◽  
Online Communities ◽  
Real Life ◽  
Statistical Properties ◽  
Clustering Coefficient ◽  
Data Sets ◽  
Life Data ◽  
Real Life Data

Social network analysis offers an understanding of our modern world, and it affords the ability to represent, analyze and even simulate complex structures. While an unweighted model can be used for online communities, trust or friendship networks should be analyzed with weighted models. To analyze social networks, it is essential to produce realistic social models. However, there are serious differences between social network models and real-life data in terms of their fundamental statistical parameters. In this paper, a genetic algorithm (GA)-based social network improvement method is proposed to produce social networks more similar to real-life data sets. First, it creates a social model based on existing studies in the literature, and then it improves the model with the proposed GA-based approach based on the similarity of the average degree, the k-nearest neighbor, the clustering coefficient, degree distribution and link overlap. This study can be used to model the structural and statistical properties of large-scale societies more realistically. The performance results show that our approach can reduce the dissimilarity between the created social networks and the real-life data sets in terms of their primary statistical properties. It has been shown that the proposed GA-based approach can be used effectively not only in unweighted networks but also in weighted networks.

Predicting Real-Life Self-Control From Brain Activity Encoding the Value of Anticipated Future Outcomes

2020 ◽  
Vol 31 (3) ◽  
pp. 268-279 ◽  
Author(s):  
Klaus-Martin Krönke ◽  
Max Wolff ◽  
Holger Mohr ◽  
Anja Kräplin ◽  
Michael N. Smolka ◽  
...  
Keyword(s):  
Brain Activity ◽  
Ecological Validity ◽  
Community Sample ◽  
Real Life ◽  
Neural Mechanisms ◽  
Self Control ◽  
Laboratory Research ◽  
Future Outcomes ◽  
Future Consequences

Deficient self-control leads to shortsighted decisions and incurs severe personal and societal costs. Although neuroimaging has advanced our understanding of neural mechanisms underlying self-control, the ecological validity of laboratory tasks used to assess self-control remains largely unknown. To increase ecological validity and to test a specific hypothesis about the mechanisms underlying real-life self-control, we combined functional MRI during value-based decision-making with smartphone-based assessment of real-life self-control in a large community sample ( N = 194). Results showed that an increased propensity to make shortsighted decisions and commit self-control failures, both in the laboratory task as well as during real-life conflicts, was associated with a reduced modulation of neural value signals in the ventromedial prefrontal cortex in response to anticipated long-term consequences. These results constitute the first evidence that neural mechanisms mediating anticipations of future consequences not only account for self-control in laboratory tasks but also predict real-life self-control, thereby bridging the gap between laboratory research and real-life behavior.

scholarly journals Finding a Weighted Positive In uence Dominating Set in E-learning Social Networks

2013 ◽  
Vol 10 (10) ◽  
pp. 2136-2145 ◽  
Author(s):  
Guangyuan Wang ◽  
Hua Wang ◽  
Xiaohui Tao ◽  
Ji Zhang ◽  
Guohun Zhu
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Learning Community ◽  
Dominating Set ◽  
Online Social Network ◽  
Real Life ◽  
Synthetic Data ◽  
Positive Influence ◽  
Data Sets ◽  
E Learning

Online social network has developed significantly in recent years. Most of current research has utilized the property of online social network to spread information and ideas. Motivated by the applications of dominating set in social networks (such as e-learning), a variation of the dominating set called positive influence dominating set (PIDS) has been studied in the literature. The existing research for PIDS problem do not take into consideration the attributes, directions and degrees of personal influence. However, these factors are very important for selecting a better PIDS. For example, in a real-life e-learning community, the attributes and the degrees of their influence between a tutor and a student are different; the relationship between two e-learning users is asymmetrical. Hence, comprehensive, deep investigation of user’s properties become an emerging and urgent issue. The focus of this study is on the degree and direction between e-learners’ influence. A novel dominating set model called weighted positive influence dominating set (WPIDS), and two selection algorithms for the WPIDS problem have been proposed. Experiments using synthetic data sets demonstrate that the proposed model and algorithms are more reasonable and effective than those of the positive influence dominating set (PIDS) without considering the key factors of weight, direction and so on.

scholarly journals Detection of Harassment Type of Cyberbullying: A Dictionary of Approach Words and Its Impact

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Syed Mahbub ◽  
Eric Pardede ◽  
A. S. M. Kayes
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Intellectual Development ◽  
Positive Impact ◽  
Real Life ◽  
Machine Learning Algorithms ◽  
Online Bullying ◽  
Predatory Behaviour ◽  
The Difference

The purpose of this paper is to analyse the effects of predatory approach words in the detection of cyberbullying and to propose a mechanism of generating a dictionary of such approach words. The research incorporates analysis of chat logs from convicted felons, to generate a dictionary of sexual approach words. By analysing data across multiple social networks, the study demonstrates the usefulness of such a dictionary of approach words in detection of online predatory behaviour through machine learning algorithms. It also shows the difference between the nature of contents across specific social network platforms. The proposed solution to detect cyberbullying and the domain of approach words are scalable to fit real-life social media, which can have a positive impact on the overall health of online social networks. Different types of cyberbullying have different characteristics. However, existing cyberbullying detection works are not targeted towards any of these specific types. This research is tailored to focus on sexual harassment type of cyberbullying and proposes a novel dictionary of approach words. Since cyberbullying is a growing threat to the mental health and intellectual development of adolescents in the society, models targeted towards the detection of specific type of online bullying or predation should be encouraged among social network researchers.

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