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.

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.

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 Role of Social Networks in Creating Public Opinion About Electronic Study

2021 ◽  
Vol 8 (1) ◽  
pp. 379-397
Author(s):  
Shanaz Sadeq Mohamad ◽  
Sara Mohsen Qadir
Keyword(s):  
Social Networks ◽  
Public Opinion ◽  
Social Network ◽  
Survey Methodology ◽  
The Public ◽  
The Social ◽  
The Subject ◽  
Study Process ◽  
Electronic Education

In line with the developments of various social networks, it has made the public see a change for all the various issues in the nation, one of which was the issue of electronic education, which has been influenced by the social networks, especially by students. Therefore, from this perspective, the researcher in the research scientifically shows the role of the social networks in creating public opinion about the process of electronic study. This research is a description, a researcher who has used the research to achieve detailed and necessary data and information about the subject of survey methodology research. Among the students of Kurdistan University, Salahaddin University and World University students are research samples of 422 students of both maleand female genders, the most important results that researchers have reached are the social networks that are a reason for creating public opinion and all The data spread through the social network to a process have created public opinion about the electronic study process, the strongest network, the Facebook social network to create public opinion in Kurdistan. In the short list of research, recommendations and suggestions have been made.

scholarly journals New forms of cyberbullying in social networks (on the example of “death community”)

2020 ◽  
pp. 43-58
Author(s):  
Elena Dmitrievna Mukhanova
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Secondary Schools ◽  
Social Problem ◽  
School Environment ◽  
Sociological Analysis ◽  
School Students ◽  
The Social ◽  
The Subject ◽  
Social Pedagogues

The subject of this research is the new forms of cyberbullying as a social problem of school environment. The goal consists in conducting a sociological analysis of the new forms of cyberbullying. The survey was conducted on the basis of Google Forms in the social network Vkontakte; it involved students of 8-11 grades, university freshmen, and students of vocational secondary schools of Nizhny Novgorod, total of 300 persons. The second survey “New forms of manifestation of cyberbullying in social networks: on the example of the phenomenon “death community” was carried out in Marc 2018, and involved school students of 5-11 grades, total of 362 persons. The acquired results demonstrate that children who were not able to find solution of their problem most often enter the “death community”. They look for solution among other members of the group, kindred in spirit, and attracted by philosophy of the groups, presented by interesting names, such as “whales swim upwards” and “whale journal”, which describe the romantic side of death, that it is a good or an achievement, rather than something scary or unknown. The scientific novelty lies in studying the problem of cyberbullying in the school environment of Nizhny Novgorod. The obtained results may be used social pedagogues and school psychologists.in formulation of recommendations for parents of “vulnerable” group of students to prevent cyberbullying.

Codiffusion of Technologies in Social Networks

2020 ◽  
Vol 12 (4) ◽  
pp. 193-228
Author(s):  
Natalia Lazzati
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Diffusion Process ◽  
Technology Diffusion ◽  
Consideration Sets ◽  
Network Structures ◽  
The Social ◽  
Over Time

This paper studies the diffusion process of two complementary technologies among people who are connected through a social network. It characterizes adoption rates over time for different initial allocations and network structures. In doing so, we provide some microfoundations for the stochastic formation of consideration sets. We are particularly interested in the following question: suppose we want to maximize technology diffusion and have a limited number of units of each of the two technologies to initially distribute—how should we allocate these units among people in the social network? (JEL D83, O33, Z13)

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 Research and analysis of messages of users of social networks using BigData technology

2019 ◽  
pp. 504-509
Author(s):  
I A Rytsarev ◽  
A V Kupriyanov ◽  
D V Kirsh ◽  
R A Paringer
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Real Time ◽  
World Cup ◽  
Word Cloud ◽  
Processing Data ◽  
The Social ◽  
The World ◽  
The Subject ◽  
The City

In this paper is dedicated to the World Cup held in the city of Samara from June 15 to July 15, 2018. As part of the work, a multithreaded collection in real time was organized, filtering and processing messages from users of the social network Twitter within the host city and its surroundings from May 15 to August 15, 2018. Then, a study was conducted of the texts of user messages on the subject of the popularity of topics and the construction of a “word cloud”. The second study was the construction of a diagram of the dynamics of the number of messages in different languages. As part of the work, modules for collecting, filtering and processing data using BigData technology were implemented.

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 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.

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