scholarly journals The impact of using Mobile Social Network Applications on Students’ Social-Life

2018 ◽  
Vol 11 (2) ◽  
pp. 1-14 ◽  
Author(s):  
Ahmed Yousif Abdelraheem ◽  
◽  
Abdelrahman Mohammed Ahmed ◽  
Keyword(s):  
Social Network ◽  
Social Life ◽  
Mobile Social Network ◽  
Network Applications ◽  
The Impact

scholarly journals A multiplex centrality metric for complex social networks: sex, social status, and family structure predict multiplex centrality in rhesus macaques

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8712 ◽  
Author(s):  
Brianne Beisner ◽  
Niklas Braun ◽  
Márton Pósfai ◽  
Jessica Vandeleest ◽  
Raissa D’Souza ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Life ◽  
Rhesus Macaques ◽  
Dominance Rank ◽  
Social Groups ◽  
Single Layer ◽  
Demographic Factors ◽  
Socio Demographic Factors ◽  
The Impact

Members of a society interact using a variety of social behaviors, giving rise to a multi-faceted and complex social life. For the study of animal behavior, quantifying this complexity is critical for understanding the impact of social life on animals’ health and fitness. Multilayer network approaches, where each interaction type represents a different layer of the social network, have the potential to better capture this complexity than single layer approaches. Calculating individuals’ centrality within a multilayer social network can reveal keystone individuals and more fully characterize social roles. However, existing measures of multilayer centrality do not account for differences in the dynamics and functionality across interaction layers. Here we validate a new method for quantifying multiplex centrality called consensus ranking by applying this method to multiple social groups of a well-studied nonhuman primate, the rhesus macaque. Consensus ranking can suitably handle the complexities of animal social life, such as networks with different properties (sparse vs. dense) and biological meanings (competitive vs. affiliative interactions). We examined whether individuals’ attributes or socio-demographic factors (sex, age, dominance rank and certainty, matriline size, rearing history) were associated with multiplex centrality. Social networks were constructed for five interaction layers (i.e., aggression, status signaling, conflict policing, grooming and huddling) for seven social groups. Consensus ranks were calculated across these five layers and analyzed with respect to individual attributes and socio-demographic factors. Generalized linear mixed models showed that consensus ranking detected known social patterns in rhesus macaques, showing that multiplex centrality was greater in high-ranking males with high certainty of rank and females from the largest families. In addition, consensus ranks also showed that females from very small families and mother-reared (compared to nursery-reared) individuals were more central, showing that consideration of multiple social domains revealed individuals whose social centrality and importance might otherwise have been missed.

scholarly journals HetInf: Social Influence Prediction With Heterogeneous Graph Neural Network

2022 ◽  
Vol 9 ◽  
Author(s):  
Liqun Gao ◽  
Haiyang Wang ◽  
Zhouran Zhang ◽  
Hongwu Zhuang ◽  
Bin Zhou
Keyword(s):  
Neural Network ◽  
Social Network ◽  
Social Influence ◽  
User Behavior ◽  
Online Advertising ◽  
Live Streaming ◽  
Network Applications ◽  
Key Factor ◽  
Study Designs ◽  
The Impact

With the continuous enrichment of social network applications, such as TikTok, Weibo, Twitter, and others, social media have become an indispensable part of our lives. Web users can participate in their favorite events or pay attention to people they like. The “heterogeneous” influence between events and users can be effectively modeled, and users’ potential future behaviors can be predicted, so as to facilitate applications such as recommendations and online advertising. For example, a user’s favorite live streaming host (user) recommends certain products (event), can we predict whether the user will buy these products in the future? The majority of studies are based on a homogeneous graph neural network to model the influence between users. However, these studies ignore the impact of events on users in reality. For instance, when users purchase commodities through live streaming channels, in addition to the factors of the host, the commodity is also a key factor that influences the behavior of users. This study designs an influence prediction model based on a heterogeneous neural network HetInf. Specifically, we first constructed the heterogeneous social influence network according to the relationship between event nodes and user nodes, then sampled the user heterogeneous subgraph for each user, extracted the relevant node features, and finally predicted the probability of user behavior through the heterogeneous neural network model. We conducted comprehensive experiments on two large social network datasets. Furthermore, the experimental results show that HetInf is significantly superior to the previous homogeneous neural network methods.

Measuring the Impact of Social Network Density on Performance

2007 ◽  
Author(s):  
Tracey E. Rizzuto ◽  
John Paul Hatala ◽  
Kara R. Jeansonne
Keyword(s):  
Social Network ◽  
Network Density ◽  
The Impact

ПРАГМАТИЧНИЙ ПОТЕНЦІАЛ НАТИВНОЇ РЕКЛАМИ: ФОРМИ, ТРЕНДИ (НА ПРИКЛАДІ НАТИВНОГО КОНТЕНТУ В СОЦІАЛЬНІЙ МЕРЕЖІ FACEBOOK ЩОДО БРЕНДА ФАКУЛЬТЕТУ ЖУРНАЛІСТИКИ ЗНУ)

2020 ◽  
pp. 113
Author(s):  
V. Kovpak ◽  
N. Trotsenko
Keyword(s):  
Social Network ◽  
Professional Training ◽  
Educational Process ◽  
Communication Strategy ◽  
Student Centered ◽  
Teacher Student ◽  
The Social ◽  
National University ◽  
High Level ◽  
The Impact

<div><p><em>The article analyzes the peculiarities of the format of native advertising in the media space, its pragmatic potential (in particular, on the example of native content in the social network Facebook by the brand of the journalism department of ZNU), highlights the types and trends of native advertising. The following research methods were used to achieve the purpose of intelligence: descriptive (content content, including various examples), comparative (content presentation options) and typological (types, trends of native advertising, in particular, cross-media as an opportunity to submit content in different formats (video, audio, photos, text, infographics, etc.)), content analysis method using Internet services (using Popsters service). And the native code for analytics was the page of the journalism department of Zaporizhzhya National University on the social network Facebook. After all, the brand of the journalism department of Zaporozhye National University in 2019 celebrates its 15th anniversary. The brand vector is its value component and professional training with balanced distribution of theoretical and practical blocks (seven practices), student-centered (democratic interaction and high-level teacher-student dialogue) and integration into Ukrainian and world educational process (participation in grant programs).</em></p></div><p><em>And advertising on social networks is also a kind of native content, which does not appear in special blocks, and is organically inscribed on one page or another and unobtrusively offers, just remembering the product as if «to the word». Popsters service functionality, which evaluates an account (or linked accounts of one person) for 35 parameters, but the main three areas: reach or influence, or how many users evaluate, comment on the recording; true reach – the number of people affected; network score – an assessment of the audience’s response to the impact, or how far the network information diverges (how many share information on this page).</em></p><p><strong><em>Key words:</em></strong><em> nativeness, native advertising, branded content, special project, communication strategy.</em></p>

An Iranian academic social network is coming (Preprint)

2019 ◽  
Author(s):  
Mohammad Dehghani ◽  
Akhondzadeh Shahin ◽  
Mesgarpour Bita ◽  
Ferdousi Reza
Keyword(s):  
Social Network ◽  
Developing Country ◽  
The People ◽  
Academic Social Network ◽  
The Impact

UNSTRUCTURED Iran has faced severe sanctions in recent years from some countries. Due to the dependence of the Iranian health industry on government payments, the health of the people in this country has suffered a lot. One of the solutions for Iran's sanctions to reduce the impact of sanctions on health is to rely on domestic researchers, but researchers in Iran are having problems. One way to reduce researchers' problems is to use the National Academic Social Network. This article describes the steps of setting up an academic social network in a developing country in four stages.

Introduction

2020 ◽  
pp. xxii-13
Author(s):  
Ryan Light ◽  
James Moody
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Bibliometric Analysis ◽  
Social Life ◽  
New Directions ◽  
Central Paradigm

This chapter provides an introduction to this volume on social networks. It argues that social network analysis is greater than a method or data, but serves as a central paradigm for understanding social life. The chapter offers evidence of the influence of social network analysis with a bibliometric analysis of research on social networks. This analysis underscores how pervasive network analysis has become and highlights key theoretical and methodological concerns. It also introduces the sections of the volume broadly structured around theory, methods, broad conceptualizations like culture and temporality, and disciplinary contributions. The chapter concludes by discussing several promising new directions in the field of social network analysis.

The Oxford Handbook of Social Networks

2020 ◽  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Social Life ◽  
Information Science ◽  
Computational Social Science ◽  
The Social ◽  
Diverse Field ◽  
The Rich

Social networks fundamentally shape our lives. Networks channel the ways that information, emotions, and diseases flow through populations. Networks reflect differences in power and status in settings ranging from small peer groups to international relations across the globe. Network tools even provide insights into the ways that concepts, ideas and other socially generated contents shape culture and meaning. As such, the rich and diverse field of social network analysis has emerged as a central tool across the social sciences. This Handbook provides an overview of the theory, methods, and substantive contributions of this field. The thirty-three chapters move through the basics of social network analysis aimed at those seeking an introduction to advanced and novel approaches to modeling social networks statistically. The Handbook includes chapters on data collection and visualization, theoretical innovations, links between networks and computational social science, and how social network analysis has contributed substantively across numerous fields. As networks are everywhere in social life, the field is inherently interdisciplinary and this Handbook includes contributions from leading scholars in sociology, archaeology, economics, statistics, and information science among others.

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