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.

Examining the Variables Affecting the Social Networks of the Elderly

2021 ◽  
pp. 523-541
Author(s):  
Didem Demir Erbil ◽  
Oya Hazer
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Successful Aging ◽  
Simple Random Sampling ◽  
The Elderly ◽  
Positive Correlation ◽  
Face To Face ◽  
The Social ◽  
Negative Predictor ◽  
Moderate Positive Correlation

This study was carried out to examine the variables affecting the social networks of the elderly. A simple random sampling method was used as a data collection method in the research. The data were collected through face-to-face interviews. The participants of the study are 500 individuals aged 60 and over from members of the Ankara branch of the Turkish Pensioners Association. According to the results of the study, there is a considerable negative correlation between social network and depression (r=-0.40, p =0.001) and loneliness (r=-0.49, p =0.001). Also, social loneliness and depression is the stronger negative predictor of the social network. Moreover, there is a considerable positive correlation between social network and perceived available support (r=-0.52, p =0.001). In addition, there is a moderate positive correlation between social network and successful aging behavior (r=-0.30, p =0.001) and life satisfaction (r=-0.35, p =0.001).

scholarly journals An efficient method to detect communities in social networks

2021 ◽  
Author(s):  
MEHJABIN KHATOON ◽  
W AISHA BANU
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Interactions ◽  
Community Detection ◽  
Average Degree ◽  
Data Sets ◽  
Time Data ◽  
Network Graph ◽  
The Social ◽  
Metric Functions

Abstract Social networks represent the social structure, which is composed of individuals having social interactions among them. The interactions between the units in a social network represent the relations of the various social contacts and aim at finding different individuals in that network, with similar interests. It is a challenging problem to detect the social interactions between individuals with comparable considerations and desires from a large social network, which can be termed as community detection. Detection of the communities from social networks has been done by other authors previously, and many community identification algorithms were also proposed, but those communities' identification has been achieved on the online available data sets. The proposed algorithm in this paper has been named as Average Degree Newman Girvan (ADNG) algorithm, which can easily identify the communities from the real-time data sets, collected from the social network websites. The approach presented here is based on first determining the average degree of the network graph and then identifying the communities using the Newman Girvan algorithm. The proposed algorithm has been compared with four community detection algorithms, i.e., Leading eigenvector (LEC) algorithm, Fastgreedy (FG) algorithm, Leiden algorithm and Kernighan-Lin (KL) algorithm based on a few metric functions. This algorithm helps to detect communities for different domains, like for any proposed government policy, online shopping products, newly launched products in a market, etc.

scholarly journals The technology of social networks and people management in the recruitment and selection of its employees

2021 ◽  
pp. 74-90
Author(s):  
Ramiro Rodrigues Sumar
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Scientific Evidence ◽  
Face To Face ◽  
The Social ◽  
Recruitment And Selection ◽  
The Impact ◽  
A Company ◽  
Virtual Mode ◽  
Selection Of

Objective: To describe the impact that social networks can have on the recruitment and selection of their employees. Question Problem: How can the social network favor the recruitment and selection of employees of a company? Methodology: Literature review. Results: The evidence of the results showed that technologies through social networks can be relevant for the recruitment and selection of people for the organization. But this recruitment should be done with a differentiated look at each type of social network by the recruiter. Final Considerations: Recruitment and selection have been changing as a traditional (face-to-face) way for the technological (virtual) mode. The study mentioned that social networks are tools capable of bringing to the recruiter candidates able to take the organization responsibly and that there are no barriers in the virtual world to find the ideal candidate. It is emphasized the importance of extending this study based on scientific evidence, in which research can be carried out in companies for the use of social networks in the monitoring of their employees.

Networks of Co-Authorship

2012 ◽  
pp. 981-994
Author(s):  
Antonio José Caulliraux Pithon ◽  
Ralfh Varges Ansuattigui ◽  
Paulo Enrique Stecklow
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Interactions ◽  
Morphological Characteristics ◽  
Network Structures ◽  
Communication Processes ◽  
The Social ◽  
The Subject

The networks are transorganizational arrangements forming a structure and, in a more abstract and generic manner, are built from the interactions between individuals and organizations. These interactions allow the emergence of network structures more related to personal ties and the types of existing social interactions between the actors. Social networks aren’t a recent enterprise, but have been the subject of deeper studies due to universalization and convergence of communication processes, fundamental to the establishment and proliferation of networks. The structure where networks are manifested calls for horizontality, where there is no formal hierarchy of the elements that comprise it, composed by nodes elements and lines elements. This article analyzes the social network of authorship of one of five Postgraduate Programs of CEFET/RJ, presenting the connections between network teachers, justifying the morphological characteristics of the network and suggesting methodologies for continuing the study for the teaching and researching networks.

Community Detection in Social Networks: Literature Review

2019 ◽  
Vol 18 (02) ◽  
pp. 1950019 ◽  
Author(s):  
Seema Rani ◽  
Monica Mehrotra
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Interactions ◽  
Community Detection ◽  
Geographic Location ◽  
Cost Effective ◽  
Research Areas ◽  
The Social ◽  
Intensive Investigation ◽  
Optimisation Techniques

Due to easy and cost-effective ways, communication has amplified many folds among humans across the globe irrespective of time and geographic location. This has led to the construction of an enormous and a wide variety of social networks that is a network of social interactions or personal relations. Social network analysis (SNA) is the inspection of social networks in order to understand the participant’s arrangement and behaviour. Discovering communities from the social network has become one of the key research areas in SNA. Communities discovered from social networks facilitate its members so as to interact with relatable people who have similar or comparable interests. However, in present time, the enormous growth of social networks demands an intensive investigation of recent work carried out for identifying community division in social networks. This paper is an attempt to enlighten the ongoing developments in the domain of Community detection (CD) for SNA. Additionally, it sheds light on the algorithms which use meta-heuristic optimisation techniques to hit upon the community structure in social networks. Further, this paper gives a comparison of proposed methods in recent years and most frequently used optimisation approaches in the domain of CD. It also describes some application areas where CD methods have been used. This guides and encourages researchers to probe and take ahead the work in the area of detecting communities from social networks.

scholarly journals An Efficient Method to Detect Communities in Social Networks

2020 ◽  
Author(s):  
MEHJABIN KHATOON ◽  
W. Aisha Banu
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Interactions ◽  
Community Detection ◽  
Average Degree ◽  
Data Sets ◽  
Time Data ◽  
Network Graph ◽  
The Social ◽  
Metric Functions

Abstract Social networks represent the social structure, which is composed of individuals having social interactions among them. The interactions between the units in a social network represent the relations of the various social contacts and aim at finding different individuals in that network, with similar interests. It is a challenging problem to detect the social interactions between individuals with comparable considerations and desires from a large social network, which can be termed as community detection. Detection of the communities from social networks has been done by other authors previously, and many community identification algorithms were also proposed, but those communities' identification has been achieved on the online available data sets. The proposed algorithm in this paper has been named as Average Degree Newman Girvan (ADNG) algorithm, which can easily identify the communities from the real-time data sets, collected from the social network websites. The approach presented here is based on first determining the average degree of the network graph and then identifying the communities using the Newman Girvan algorithm. The proposed algorithm has been compared with three community detection algorithms, i.e., Leading eigenvector (LEC) algorithm, Fastgreedy (FG) algorithm, and Kernighan-Lin (KL) algorithm based on a few metric functions. This algorithm helps to detect communities for different domains, like for any proposed government policy, online shopping products, newly launched products in a market, etc.

scholarly journals Communication in Social Networks: Factors of Potential Conflicts

2020 ◽  
pp. 21-25
Author(s):  
Galina Ivanenko
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Self Control ◽  
Language Models ◽  
Discursive Practice ◽  
Public And Private ◽  
Face To Face ◽  
Network Availability ◽  
The Social ◽  
Communication Time

Social networks are a relatively new type of communication that has its own specificity. The article raises the problem of communication in the social network in terms of potentialconflicts. The analysis of modern judicial practice has shown that the number of proceedings in which communication in the social network has played a significant role is increasing. The factors of network interaction that influence the emergence and development of conflict are considered. The factors of conflict communication in social networks include the following: 1) lack of differentiation between the features publicity/ privacy, resulting from the hybridization of language models and means of public and private communication; 2) lack of non-verbal means of communication, which is partly, but not fully enough, compensated through specific ideographic sign system ofthe Internet communication; 3) round-the-clock network availability and, in this regard,extended communication time, which generatesunlimited communication,provoking decrease of self-control and diversity in time; 4) the weakening of traditional ethical prohibitions for communicationat distance as a result of an attempt to use various images and cultural codes that reflect them in face-to-face communication, including those that differ from those inherent to face-to-face communication; 5) stylistic degradation of the speech culture of communication, manifested  in two types: the use of lower-range vocabulary to imitate colloquial discursive practice and the use of colloquial, slang, taboo language units for the purpose of speech aggression,verbal insult. Awareness of the causes of speech conflicts in social networks, often leading to the violation of  legal rules and to legal proceedings, minimizes the risk of its occurrence and makes it possible to get the maximum benefit from the new communication means provided by the technological progress.

scholarly journals Persistent Political Engagement: Social Interactions and the Dynamics of Protest Movements

2021 ◽  
Vol 3 (2) ◽  
pp. 233-250
Author(s):  
Leonardo Bursztyn ◽  
Davide Cantoni ◽  
David Y. Yang ◽  
Noam Yuchtman ◽  
Y. Jane Zhang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Interactions ◽  
Political Engagement ◽  
Political Movements ◽  
Political Movement ◽  
Protest Movements ◽  
Persistent Effect ◽  
The Social

We study the causes of sustained participation in political movements. To identify the persistent effect of protest participation, we randomly indirectly incentivize Hong Kong university students into participation in an antiauthoritarian protest. To identify the role of social networks, we randomize this treatment’s intensity across major-cohort cells. We find that incentives to attend one protest within a political movement increase subsequent protest attendance but only when a sufficient fraction of an individual’s social network is also incentivized to attend the initial protest. One-time mobilization shocks have dynamic consequences, with mobilization at the social network level important for sustained political engagement. (JEL D72, D74, I23, Z13)

Emotive Trending And Tracking of Tweets

2017 ◽  
Vol 7 (11) ◽  
pp. 25
Author(s):  
Sanjay Chhataru Gupta
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
The Social ◽  
The World ◽  
Complex Events

Popularity of the social media and the amount of importance given by an individual to social media has significantly increased in last few years. As more and more people become part of the social networks like Twitter, Facebook, information which flows through the social network, can potentially give us good understanding about what is happening around in our locality, state, nation or even in the world. The conceptual motive behind the project is to develop a system which analyses about a topic searched on Twitter. It is designed to assist Information Analysts in understanding and exploring complex events as they unfold in the world. The system tracks changes in emotions over events, signalling possible flashpoints or abatement. For each trending topic, the system also shows a sentiment graph showing how positive and negative sentiments are trending as the topic is getting trended.

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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