scholarly journals USING PAGERANK AND SOCIAL NETWORK ANALYSIS TO SPECIFY MENTAL HEALTH FACTORS

2021 ◽  
Vol 1 ◽  
pp. 3379-3388
Author(s):  
Arsineh Boodaghian Asl ◽  
Jayanth Raghothama ◽  
Adam Darwich ◽  
Sebastiaan Meijer
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Well Being ◽  
Influential Factors ◽  
System Perspective ◽  
Novel Approach ◽  
Wide Range ◽  
Mental Health Factors ◽  
Mental Well Being

AbstractVarious factors influence mental well-being, and span individual, social and familial levels. These factors are connected in many ways, forming a complex web of factors and providing pathways for developing programs to improve well-being and for further research. These factors can be studied individually using traditional methods and mapped together to be analyzed holistically from a complex system perspective. This study provides a novel approach using PageRank and social network analysis to understand such maps. The motives are: (1) to realize the most influential factors in such complex networks, (2) to understand factors that influence variations from different network aspects. A previously developed map for children's mental well-being was adopted to evaluate the approach. To achieve our motives, we have developed an approach using PageRank and Social Network Analysis. The results indicate that regardless of the network scale, two key factors called "Quantity and Quality of Relationships" and "Advocacy" can influence children's mental well-being significantly. Moreover, the divergence analysis reveals that one factor, "Recognition/Value Placed on well-being at School" causes a wide range of diffusion throughout the system.

scholarly journals A Brief Overview of Social Network Analysis and its Current State within Romanian Sociology

2013 ◽  
Vol 3 (3) ◽  
pp. 5-11
Author(s):  
Marian-Gabriel Hâncean
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
International Relations ◽  
Early Spring ◽  
Research Topics ◽  
Political Sciences ◽  
Current State ◽  
Wide Range ◽  
Network Studies

Abstract The field of social network studies has been growing within the last 40 years, gathering scholars from a wide range of disciplines (biology, chemistry, geography, international relations, mathematics, political sciences, sociology etc.) and covering diverse substantive research topics. Using Google metrics, the scientific production within the field it is shown to follow an ascending trend since the late 60s. Within the Romanian sociology, social network analysis is still in his early spring, network studies being low in number and rather peripheral. This note gives a brief overview of social network analysis and makes some short references to the current state of the network studies within Romanian sociology

Aspects of Various Community Detection Algorithms in Social Network Analysis

2019 ◽  
pp. 327-341
Author(s):  
Nicole Belinda Dillen ◽  
Aruna Chakraborty
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Detection ◽  
Overlapping Community Detection ◽  
Detection Algorithms ◽  
Novel Approach ◽  
Recent Developments ◽  
Overlapping Community ◽  
Related Individuals

One of the most important aspects of social network analysis is community detection, which is used to categorize related individuals in a social network into groups or communities. The approach is quite similar to graph partitioning, and in fact, most detection algorithms rely on concepts from graph theory and sociology. The aim of this chapter is to aid a novice in the field of community detection by providing a wider perspective on some of the different detection algorithms available, including the more recent developments in this field. Five popular algorithms have been studied and explained, and a recent novel approach that was proposed by the authors has also been included. The chapter concludes by highlighting areas suitable for further research, specifically targeting overlapping community detection algorithms.

Utility of social network analysis for primate behavioral management and well-being

2008 ◽  
Vol 109 (2-4) ◽  
pp. 396-405 ◽  
Author(s):  
Brenda McCowan ◽  
Kristen Anderson ◽  
Allison Heagarty ◽  
Ashley Cameron
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Well Being ◽  
Behavioral Management

scholarly journals Explaining the Development of Information Security Climate and an Information Security Support Network: A Longitudinal Social Network Analysis

2019 ◽  
Vol 23 ◽  
Author(s):  
Duy Dang-Pham ◽  
Karlheinz Kautz ◽  
Siddhi Pittayachawan ◽  
Vince Bruno
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Information Security ◽  
Support Network ◽  
Novel Approach ◽  
Individual Cognition ◽  
Climate Perceptions ◽  
Personal Advice ◽  
Practical Recommendations

Behavioural information security (InfoSec) research has studied InfoSec at workplaces through the employees’ perceptions of InfoSec climate, which is determined by observable InfoSec practices performed by their colleagues and direct supervisors. Prior studies have identified the antecedents of a positive InfoSec climate, in particular socialisation through the employees’ discussions of InfoSec-related matters to explain the formation of InfoSec climate based on the employees’ individual cognition. We conceptualise six forms of socialisation as six networks, which comprise employees’ provisions of (1) work advice, (2) organisational updates, (3) personal advice, (4) trust for expertise, (5) InfoSec advice, and (6) InfoSec troubleshooting support. The adoption of a longitudinal social network analysis (SNA), called stochastic actor-oriented modelling (SAOM), enabled us to analyse the changes in the socialising patterns and the InfoSec climate perceptions over time. Consequently, this analysis explains the forming mechanisms of the employees’ InfoSec climate perceptions as well as their socialising process in greater detail. Our findings in relation to the forming mechanisms of InfoSec-related socialisation and InfoSec climate, provide practical recommendations to improve organisational InfoSec. This includes identifying influential employees to diffuse InfoSec knowledge within a workplace. Additionally, this research proposes a novel approach for InfoSec behavioural research through the adoption of SNA methods to study InfoSec-related phenomena.

Studying Discourse as Social Interaction: The Potential of Social Network Analysis for Discourse Studies

2018 ◽  
Vol 47 (6) ◽  
pp. 375-383 ◽  
Author(s):  
Christopher J. Wagner ◽  
María González-Howard
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Classroom Discourse ◽  
Learning Opportunities ◽  
Social Phenomenon ◽  
Novel Approach ◽  
Discourse Studies ◽  
Classroom Structures

Education researchers have extensively studied classroom discourse as a way to understand classroom structures and learning. This article proposes the use of social network analysis (SNA) as a method for discourse studies in education. SNA enables us to learn about the connections between persons and the patterns of relations within groups. This presents a novel approach to the study of discourse that may more accurately reflect current understandings of discourse as a social phenomenon. This article explains the theoretical links between SNA and the concept of discourse in education and then considers how SNA can be used to examine classroom discourse. A brief overview of promising methods is presented to provide examples of how SNA can be applied to discourse data. This article argues that continued exploration and applications of SNA could yield more complex understandings of the role of discourse in learning opportunities and outcomes.

scholarly journals Addressing problematic consumption: an action research experience from the Social Network Analysis

AWARI ◽  
2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Juan José Vera ◽  
Nicolás Barroso
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Community Treatment ◽  
Social Intervention ◽  
Social Suffering ◽  
Research Experience ◽  
Treatment Groups ◽  
The Social ◽  
Wide Range

This paper is classified under the trend of studies that make use of ‘Social Network Analysis’ (SNA) to serve as guidelines for social intervention. Within the field of SNA, work has been carried out based on what is referred to as the socio-centric approach, with the aim of revealing a type of complete network, the Subjective Communities Networks, which are built from Community Treatment Groups pertaining to the Argentine Office of Drug-related Comprehensive Policies (SEDRONAR for its Spanish acronym) in order to address problematic abuse in socially vulnerable backgrounds. These groups belong to the ECO2 model, which was devised to intervene in a wide range of social suffering phenomena, and uses the SNA as a theoretical and methodological viewpoint for assessing people and communities. This thought is an attempt to answer the following question: how does SNA help formulate social intervention strategies for SEDRONAR groups in the Province of Mendoza?

scholarly journals Social Network Analysis in Russian Literary Studies

2020 ◽  
pp. 517-536
Author(s):  
Frank Fischer ◽  
Daniil Skorinkin
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Literary Texts ◽  
Literary Studies ◽  
War And Peace ◽  
The Social ◽  
Wide Range ◽  
Russian Literary ◽  
Simple Fashion

AbstractNetwork analysis as a method has applications in a wide range of fields from physics to epidemiology and from sociology to political science, and in the meantime has also reached the literary studies. Networks can be leveraged to examine intertextual relations or even artistic influences, but the main application so far has been the analysis of social formations and character interactions within fictional worlds. To make this possible, texts have to be formalized into a set of nodes and edges, where nodes represent characters and edges describe the relations between these characters in a very simple fashion: Do they or don’t they interact? Based on a selection of Russian plays and Tolstoy’s novel War and Peace, we will describe approaches to the social network analysis of literary texts.

scholarly journals Cannabis Use and Emotional Intelligence in Adolescents during COVID-19 Confinement: A Social Network Analysis Approach

2021 ◽  
Vol 13 (23) ◽  
pp. 12954
Author(s):  
María Cristina Martínez-Fernández ◽  
Isaías García-Rodríguez ◽  
Natalia Arias-Ramos ◽  
Rubén García-Fernández ◽  
Bibiana Trevissón-Redondo ◽  
...  
Keyword(s):  
Emotional Intelligence ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Well Being ◽  
Cannabis Use ◽  
Cannabis User ◽  
Significant Variable ◽  
Negative Consequences ◽  
Friendship Network

Confinement by COVID-19 had negative consequences on adolescent mental health, including increased cannabis use. Cannabis is related to variables that influence health and well-being. Emotional Intelligence is associated with adaptive coping styles, peer relationships, and social–emotional competencies. In adolescence, peer selection plays a unique role in the initiation of substance use. However, there are no studies during a confinement stage that analyse the relationships between networks, Emotional Intelligence, and cannabis use. The aim of this paper is to describe and analyse the consumption and friendship networks of an adolescent classroom and their relationship with Emotional Intelligence, cannabis use, and gender during COVID-19 confinement. Participants completed different questionnaires for Emotional Intelligence, cannabis use, and the consumption and friendship network. The sample consisted of 21 students from 10th grade, of which 47.6% were consumers. The friendship network correlates with the consumption network, and significant associations between emotional repair and being a cannabis user. The regression model points to the friendship network as a significant variable in predicting the classroom use network. This study highlights the role of the Social Network Analysis in predicting consumption networks during a COVID-19 confinement stage and serves as a tool for cannabis use prevention interventions in a specific population.

Mixed Method Social Network Analysis

2016 ◽  
Vol 20 (2) ◽  
pp. 268-298 ◽  
Author(s):  
Trenton A. Williams ◽  
Dean A. Shepherd
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Organizational Behavior ◽  
Mixed Method ◽  
New Ventures ◽  
Organizational History ◽  
General Application ◽  
Social Actors ◽  
Novel Approach

This article outlines a mixed method approach to social network analysis combining techniques of organizational history development, inductive data structuring, and content analysis to offer a novel approach for network data construction and analysis. This approach provides researchers with a number of benefits over traditional sociometric or other interpersonal methodologies including the ability to investigate networks of greater scope, broader access to diverse social actors, reduced informant bias, and increased capability for longitudinal designs. After detailing this approach, we apply the method on a sample of 143 new ventures and suggest opportunities for general application in entrepreneurship, strategic management, and organizational behavior research.

scholarly journals Social Network Analysis using RLVECN: Representation Learning via Knowledge-Graph Embeddings and Convolutional Neural-Network

2020 ◽  
Author(s):  
Bonaventure C. Molokwu ◽  
Ziad Kobti
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Representation Learning ◽  
Knowledge Graph ◽  
Graph Embeddings ◽  
Social Graph ◽  
Wide Range ◽  
Social Graphs ◽  
Prediction Problems

Social Network Analysis (SNA) has become a very interesting research topic with regard to Artificial Intelligence (AI) because a wide range of activities, comprising animate and inanimate entities, can be examined by means of social graphs. Consequently, classification and prediction tasks in SNA remain open problems with respect to AI. Latent representations about social graphs can be effectively exploited for training AI models in a bid to detect clusters via classification of actors as well as predict ties with regard to a given social network. The inherent representations of a social graph are relevant to understanding the nature and dynamics of a given social network. Thus, our research work proposes a unique hybrid model: Representation Learning via Knowledge-Graph Embeddings and ConvNet (RLVECN). RLVECN is designed for studying and extracting meaningful representations from social graphs to aid in node classification, community detection, and link prediction problems. RLVECN utilizes an edge sampling approach for exploiting features of the social graph via learning the context of each actor with respect to its neighboring actors.

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