Social brain network predicts real-world social network in individuals with social anhedonia

2021 ◽  
Vol 317 ◽  
pp. 111390
Author(s):  
Yi-jing Zhang ◽  
Xin-lu Cai ◽  
Hui-xin Hu ◽  
Rui-ting Zhang ◽  
Yi Wang ◽  
...  
Keyword(s):  
Social Network ◽  
Real World ◽  
Brain Network ◽  
Social Anhedonia ◽  
Social Brain

Social brain network correlates with real-life social network in individuals with schizophrenia and social anhedonia

2021 ◽  
Vol 232 ◽  
pp. 77-84
Author(s):  
Yi-jing Zhang ◽  
Cheng-cheng Pu ◽  
Yong-ming Wang ◽  
Rui-ting Zhang ◽  
Xin-lu Cai ◽  
...  
Keyword(s):  
Social Network ◽  
Real Life ◽  
Brain Network ◽  
Social Anhedonia ◽  
Social Brain

scholarly journals The emergence of a theta social brain network during infancy

NeuroImage ◽  
2021 ◽  
pp. 118298
Author(s):  
Bauke van der Velde ◽  
Tonya White ◽  
Prof. Chantal Kemner
Keyword(s):  
Brain Network ◽  
Social Brain

SARCP - Exploiting Cyber-Attack Prediction Through Socially-Aware Recommendation

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Social Network ◽  
Real World ◽  
Cyber Security ◽  
Betweenness Centrality ◽  
Cyber Attacks ◽  
Experimental Results ◽  
Cyber Attack ◽  
Defence Mechanisms ◽  
Network Measure ◽  
Recommender Algorithm

In the domain of cyber security, the defence mechanisms of networks has traditionally been placed in a reactionary role. Cyber security professionals are therefore disadvantaged in a cyber-attack situation due to the fact that it is vital that they maneuver such attacks before the network is totally compromised. In this paper, we utilize the Betweenness Centrality network measure (social property) to discover possible cyber-attack paths and then employ computation of similar personality of nodes/users to generate predictions about possible attacks within the network. Our method proposes a social recommender algorithm called socially-aware recommendation of cyber-attack paths (SARCP), as an attack predictor in the cyber security defence domain. In a social network, SARCP exploits and delivers all possible paths which can result in cyber-attacks. Using a real-world dataset and relevant evaluation metrics, experimental results in the paper show that our proposed method is favorable and effective.

scholarly journals Online social network size is reflected in human brain structure

2011 ◽  
Vol 279 (1732) ◽  
pp. 1327-1334 ◽  
Author(s):  
R. Kanai ◽  
B. Bahrami ◽  
R. Roylance ◽  
G. Rees
Keyword(s):  
Social Network ◽  
Social Networking ◽  
Real World ◽  
Brain Structure ◽  
Grey Matter ◽  
Online Social Network ◽  
Network Size ◽  
Matter Density ◽  
Web Based ◽  
Social Network Size

The increasing ubiquity of web-based social networking services is a striking feature of modern human society. The degree to which individuals participate in these networks varies substantially for reasons that are unclear. Here, we show a biological basis for such variability by demonstrating that quantitative variation in the number of friends an individual declares on a web-based social networking service reliably predicted grey matter density in the right superior temporal sulcus, left middle temporal gyrus and entorhinal cortex. Such regions have been previously implicated in social perception and associative memory, respectively. We further show that variability in the size of such online friendship networks was significantly correlated with the size of more intimate real-world social groups. However, the brain regions we identified were specifically associated with online social network size, whereas the grey matter density of the amygdala was correlated both with online and real-world social network sizes. Taken together, our findings demonstrate that the size of an individual's online social network is closely linked to focal brain structure implicated in social cognition.

scholarly journals Study of Automotive Brands Popularity in Indonesia Using Twitter Data

2016 ◽  
Vol 3 (1) ◽  
pp. 23-33
Author(s):  
Stevent Efendi ◽  
Alva Erwin ◽  
Kho I Eng
Keyword(s):  
Social Media ◽  
Social Network ◽  
Sentiment Analysis ◽  
Automotive Industry ◽  
Real World ◽  
The Internet ◽  
Brand Preference ◽  
Twitter Data ◽  
Wide Range ◽  
Widespread Phenomenon

Social media has been a widespread phenomenon in the recent years. People shared a lot of thought in social media, and these data posted on the internet could be used for study and researches. As one of the fastest growing social network, Twitter is a particularly popular social media to be studied because it allows researchers to access their data. This research will look the correlation between Twitter chatter of a brand and the sales of brands in Indonesia. Factors such as sentiment and tweet rate are expected to be able to predict the popularity of a brand. Being one of the biggest industries in Indonesia, automotive industry is an interesting subject to study. A wide range of people buys vehicles, and even gather as communities based on their car or motorcycle brand preference. The Twitter results of sentiment analysis and tweet rate will be compared with real world sales results published by GAIKINDO and AISI.

scholarly journals Who comes to mind? Dynamic construction of social networks

2020 ◽  
Author(s):  
Joseph Bayer ◽  
Neil Anthony Lewis ◽  
Jonathan Stahl
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Real World ◽  
Dynamic Process ◽  
Social Groups ◽  
Social Resources ◽  
Personal Relationships ◽  
Cognitive Mechanisms ◽  
Relational Structures ◽  
Network Technologies

Much remains unknown about moment-to-moment social-network cognition — that is, who comes to mind as we go about our day-to-day lives. Responding to this void, we describe the real-time construction of cognitive social networks. First, we outline the types of relational structures that comprise momentary networks, distinguishing the roles of personal relationships, social groups, and mental sets. Second, we discuss the cognitive mechanisms that determine which individuals are activated — and which are neglected — through a dynamic process. Looking forward, we contend that these overlooked mechanisms need to be considered in light of emerging network technologies. Finally, we chart the next steps for understanding social-network cognition across real-world contexts, along with the built-in implications for social resources and intergroup disparities.

Flawed Security of Social Network of Things

2018 ◽  
pp. 65-86
Author(s):  
Rohit Anand ◽  
Akash Sinha ◽  
Abhishek Bhardwaj ◽  
Aswin Sreeraj
Keyword(s):  
Social Network ◽  
Real World ◽  
Personal Data ◽  
Dynamic Structure ◽  
Security And Privacy ◽  
Human Intervention ◽  
Security Measures ◽  
The Social ◽  
Data Capturing ◽  
Shared Network

This chapter deals with the security flaws of social network of things. The network of things (NoT) is a dynamic structure that is basically an interface of real world and virtual world having capabilities of collection and sharing data over a shared network. The social network of things (SNoT) is a versatile way of connecting virtual and real world. Like any other device connected to internet, objects in SNoT are also vulnerable to the various security and privacy attacks. Generally, to secure Social Network of Things in which human intervention is absent, data capturing devices must be avoided. Types of security attacks that are huge threats to NoT as well as SNoT will be discussed in the chapter. The huge collection of information without necessary security measures allows an intruder to misuse the personal data of owner. Different types of attacks with reference to the different layers are also discussed in detail. The best possible potential solutions for the security of devices in SNoT will be considered.

Competence Management over Social Networks Through Dynamic Taxonomies

2013 ◽  
pp. 103-120
Author(s):  
Giuseppe Berio ◽  
Antonio Di Leva ◽  
Mounira Harzallah ◽  
Giovanni M. Sacco
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Real World ◽  
Reference Model ◽  
Effective Solution ◽  
Network Information ◽  
The Social ◽  
Competence Management

The exploitation and integration of social network information in a competence reference model (CRAI, Competence, Resource, Aspect, Individual) are discussed. The Social-CRAI model, which extends CRAI to social networks, provides an effective solution to this problem and is discussed in detail. Finally, dynamic taxonomies, a model supporting explorative conceptual search, are introduced and their use in the context of the Social-CRAI model for exploring retrieved information available in social networks is discussed. A real-world example is provided.

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