Reconstruction of a Real World Social Network using the Potts Model and Loopy Belief Propagation

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.

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Online social network size is reflected in human brain structure

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2011.1959 ◽

2011 ◽

Vol 279 (1732) ◽

pp. 1327-1334 ◽

Cited By ~ 154

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.

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Study of Automotive Brands Popularity in Indonesia Using Twitter Data

Journal of Applied Information, Communication and Technology ◽

10.33555/ejaict.v3i1.91 ◽

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.

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Who comes to mind? Dynamic construction of social networks

10.31234/osf.io/fkjn3 ◽

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.

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