scholarly journals The Research of Social Network Analysis on College Students' Interactive Relations

2021 ◽  
Vol 15 (2) ◽  
pp. 49-59
Author(s):  
Chang Chen ◽  
Min Chen
Keyword(s):  
College Students ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Student Affairs ◽  
Interpersonal Relations ◽  
Interaction Network ◽  
Information Support ◽  
Limited Information ◽  
New Information

Nowadays the number of college students' suicides are increasing for the insufficient social support or poor interpersonal relations. Furthermore, not much attention has been concerned to students' interpersonal relations when handling student affairs and only very limited information about students' interaction network is available. This paper studies the peer network of college students by using the tool of social network analysis. And it aims to serve as instrumental support for students to foster and develop harmonious interpersonal relations. It offers new information for school counsellor to better handle student affairs and provides information support for the carrying out of moral and ideological guidance for students.

scholarly journals Analisis Akun Twitter Berpengaruh terkait Covid-19 menggunakan Social Network Analysis

2021 ◽  
Vol 5 (4) ◽  
pp. 697-704
Author(s):  
Aprillian Kartino ◽  
M. Khairul Anam ◽  
Rahmaddeni ◽  
Junadhi
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Information Dissemination ◽  
Interaction Network ◽  
Analysis Method ◽  
The Social ◽  
The World ◽  
High Degree

Covid-19 is a disease of the virus that is shaking the world and has been designated by WHO as a pandemic. This case of Covid-19 can be a place of dissemination of disinformation that can be utilized by some parties. The dissemination of information in this day and age has turned to the internet, namely social media, Twitter is one of the social media that is often used by Indonesians and the data can be analyzed. This study uses the social network analysis method, conducted to be able to find nodes that affect the ongoing interaction in the interaction network of information dissemination related to Covid-19 in Indonesia and see if the node is directly proportional to the value of its popularity. As well as to know in identifying the source of Covid-19 information, whether dominated by competent Twitter accounts in their fields. The data examined 19,939 nodes and 12,304 edges were taken from data provided by the web academic.droneemprit.id on the project "Analisis Opini Persebaran Virus Corona di Media Sosial", using the period of December 2019 to December 2020 on social media Twitter. The results showed that the @do_ra_dong account is an influential actor with the highest degree centrality of 860 and the @detikcom account is the actor with the highest popularity value of follower rank of 0.994741605. Thus actors who have a high degree of centrality value do not necessarily have a high follower rank value anyway. The study ignores if there are buzzer accounts on Twitter.  

Preparing Trainee Translators for the Job Market

2018 ◽  
pp. 137-167
Author(s):  
Bryan J. Robinson ◽  
M. Dolores Olvera-Lobo
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Survey ◽  
Interpersonal Relations ◽  
Team Cohesion ◽  
Job Market ◽  
Friendship Relations ◽  
Professional Workplace

Competence-based learning contrasts radically with content-focused education. Today's undergraduate programmes take a multidisciplinary approach that imbues learning with input from the professional workplace. This chapter describes possibly the first social network analysis of trainee translators participating in an intensive, randomised teamwork experience centred on project-based, cooperative learning. An online survey gathered data and perceptions of the teamwork experience and of interpersonal relations. Participants describe friendship relations, the quality of their peers' performance in professional roles, and their preferences with regard to the roles, and these are contrasted within the teams. These indicators of intra-team cohesion are compared with course-final achievement. Results indicate that the strengthening of friendship ties accompanies greater cohesion in teams and may be associated with higher achievement. This suggests that a multidisciplinary focus on teamwork competences enhances learners' professional prospects.

Who is the boss? Identifying key roles in telecom fraud network via centrality-guided deep random walk

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi-Chun Chang ◽  
Kuan-Ting Lai ◽  
Seng-Cho T. Chou ◽  
Wei-Chuan Chiang ◽  
Yuan-Chen Lin
Keyword(s):  
Social Network ◽  
Random Walk ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Random Walks ◽  
Interpersonal Relationships ◽  
Interaction Network ◽  
Data Set ◽  
Content Type ◽  
Structural Equivalence

PurposeTelecommunication (telecom) fraud is one of the most common crimes and causes the greatest financial losses. To effectively eradicate fraud groups, the key fraudsters must be identified and captured. One strategy is to analyze the fraud interaction network using social network analysis. However, the underlying structures of fraud networks are different from those of common social networks, which makes traditional indicators such as centrality not directly applicable. Recently, a new line of research called deep random walk has emerged. These methods utilize random walks to explore local information and then apply deep learning algorithms to learn the representative feature vectors. Although effective for many types of networks, random walk is used for discovering local structural equivalence and does not consider the global properties of nodes.Design/methodology/approachThe authors proposed a new method to combine the merits of deep random walk and social network analysis, which is called centrality-guided deep random walk. By using the centrality of nodes as edge weights, the authors’ biased random walks implicitly consider the global importance of nodes and can thus find key fraudster roles more accurately. To evaluate the authors’ algorithm, a real telecom fraud data set with around 562 fraudsters was built, which is the largest telecom fraud network to date.FindingsThe authors’ proposed method achieved better results than traditional centrality indices and various deep random walk algorithms and successfully identified key roles in a fraud network.Research limitations/implicationsThe study used co-offending and flight record to construct a criminal network, more interpersonal relationships of fraudsters, such as friendships and relatives, can be included in the future.Originality/valueThis paper proposed a novel algorithm, centrality-guided deep random walk, and applied it to a new telecom fraud data set. Experimental results show that the authors’ method can successfully identify the key roles in a fraud group and outperform other baseline methods. To the best of the authors’ knowledge, it is the largest analysis of telecom fraud network to date.

Protecting Privacy by Secure Computation

2012 ◽  
pp. 268-284
Author(s):  
Florian Kerschbaum ◽  
Daniel Funke
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Relationship Management ◽  
Secure Computation ◽  
Customer Relationship ◽  
System A ◽  
Input And Output ◽  
Social Network Structure ◽  
New Information

We consider collaborative social network analysis without revealing private inputs of the participants. This problem arises in criminal investigations of federal police organization where single organizations may not reveal their data without probable cause, but the aggregation of all data entails new information, such as the entire social network structure. We present algorithms for securely computing either the entire, anonymized graph or only specific metrics for individuals. We use secure computation protocols to disclose nothing, but the output of the analysis, i.e. anything that cannot be derived from one’s input and output – including other parties’ input – remains private. We have implemented a prototype for SAP’s investigative case management system – a derivate of its customer relationship management.

scholarly journals Empiricism and Theorizing in Epidemiology and Social Network Analysis

2011 ◽  
Vol 2011 ◽  
pp. 1-5 ◽  
Author(s):  
Richard Rothenberg ◽  
Elizabeth Costenbader
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Empirical Data ◽  
Transmission Dynamics ◽  
Scientific Creativity ◽  
New Information ◽  
Testing Theory ◽  
The Relationship ◽  
Insight Into

The connection between theory and data is an iterative one. In principle, each is informed by the other: data provide the basis for theory that in turn generates the need for new information. This circularity is reflected in the notion of abduction, a concept that focuses on the space between induction (generating theory from data) and deduction (testing theory with data). Einstein, in the 1920s, placed scientific creativity in that space. In the field of social network analysis, some remarkable theory has been developed, accompanied by sophisticated tools to develop, extend, and test the theory. At the same time, important empirical data have been generated that provide insight into transmission dynamics. Unfortunately, the connection between them is often tenuous and the iterative loop is frayed. This circumstance may arise both from data deficiencies and from the ease with which data can be created by simulation. But for whatever reason, theory and empirical data often occupy different orbits. Fortunately, the relationship, while frayed, is not broken, to which several recent analyses merging theory and extant data will attest. Their further rapprochement in the field of social network analysis could provide the field with a more creative approach to experimentation and inference.

Protecting Privacy by Secure Computation

2012 ◽  
pp. 565-580
Author(s):  
Florian Kerschbaum ◽  
Daniel Funke
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Relationship Management ◽  
Secure Computation ◽  
Customer Relationship ◽  
System A ◽  
Input And Output ◽  
Social Network Structure ◽  
New Information

We consider collaborative social network analysis without revealing private inputs of the participants. This problem arises in criminal investigations of federal police organization where single organizations may not reveal their data without probable cause, but the aggregation of all data entails new information, such as the entire social network structure. We present algorithms for securely computing either the entire, anonymized graph or only specific metrics for individuals. We use secure computation protocols to disclose nothing, but the output of the analysis, i.e. anything that cannot be derived from one’s input and output – including other parties’ input – remains private. We have implemented a prototype for SAP’s investigative case management system – a derivate of its customer relationship management.

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