Transitive node similarity for link prediction in social networks with positive and negative links

2010 ◽  
Author(s):  
Panagiotis Symeonidis ◽  
Eleftherios Tiakas ◽  
Yannis Manolopoulos
Keyword(s):  
Social Networks ◽  
Link Prediction ◽  
Node Similarity ◽  
Negative Links

scholarly journals Social Media Addiction and Personality: A Review of Research

2021 ◽  
Vol 18 (3) ◽  
pp. 607-630
Author(s):  
Viktor P. Sheinov
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Foreign Language ◽  
Self Esteem ◽  
Way Of Life ◽  
Face To Face ◽  
Social Media Addiction ◽  
Physical Health Problems ◽  
Negative Links

Social networks are taking up more and more place in the daily life of modern people, becoming an integral part of our existence. At the same time, the role of social networks is constantly growing along with the rapid growth in the number of their active users. As online interaction for many has become more used than face-to-face communication, social networks have begun to seriously affect the way of life, communication, interests and psychology of people. The use of social networks is growing exponentially and has covered more than a third of the worlds population; therefore, researchers from different countries are actively studying social networks. Considerable empirical data has been accumulated that requires generalization and understanding, which is the purpose of this review. We found positive links between social media addiction and depression, anxiety, stress, neuroticism, emotional problems, low self-esteem, cyber-victimization, physical health problems, mental disorders, loneliness, procrastination, smartphone and internet addiction, and infidelity in relationships. Negative links were revealed between social media addiction and life satisfaction, academic performance of schoolchildren and students, labor productivity and commitment to the organization of its employees, social capital, and age. The main reason for social media addiction is the need for communication, and women are generally more active in social networks than men. This review provides only those links of social media addiction that have been established in a number of studies conducted in different countries. The presented results were obtained abroad using foreign language questionnaires that determine social media addiction. The lack of such a reliable and valid tool among Russian-speaking psychologists has become a serious factor hindering the conduct of similar domestic research. With this in view, the author developed a specially designed social media addiction questionnaire.

scholarly journals DeepOSN: Bringing deep learning as malicious detection scheme in online social network

2020 ◽  
Vol 9 (1) ◽  
pp. 146
Author(s):  
Putra Wanda ◽  
Marselina Endah Hiswati ◽  
Huang J. Jie
Keyword(s):  
Social Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Online Social Networks ◽  
Link Prediction ◽  
Online Social Network ◽  
Detection Scheme ◽  
Malicious Activity ◽  
Manual Analysis ◽  
Learning Techniques

Manual analysis for malicious prediction in Online Social Networks (OSN) is time-consuming and costly. With growing users within the environment, it becomes one of the main obstacles. Deep learning is growing algorithm that gains a big success in computer vision problem. Currently, many research communities have proposed deep learning techniques to automate security tasks, including anomalous detection, malicious link prediction, and intrusion detection in OSN. Notably, this article describes how deep learning makes the OSN security technique more intelligent for detecting malicious activity by establishing a classifier model.

scholarly journals Group Relationship Mining of College Students Based on Predictive Social Network

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Huazhang Liu
Keyword(s):  
College Students ◽  
Social Networks ◽  
Social Network ◽  
Complex Network ◽  
Link Prediction ◽  
Rapid Development ◽  
Combination Method ◽  
Prediction Algorithm ◽  
The Social ◽  
Random Block

With the rapid development of the Internet, social networks have shown an unprecedented development trend among college students. Closer social activities among college students have led to the emergence of college students with new social characteristics. The traditional method of college students’ group classification can no longer meet the current demand. Therefore, this paper proposes a social network link prediction method-combination algorithm, which combines neighbor information and a random block. By mining the social networks of college students’ group relationships, the classification of college students’ groups can be realized. Firstly, on the basis of complex network theory, the essential relationship of college student groups under a complex network is analyzed. Secondly, a new combination algorithm is proposed by using the simplest linear combination method to combine the proximity link prediction based on neighbor information and the likelihood analysis link prediction based on a random block. Finally, the proposed combination algorithm is verified by using the social data of college students’ networks. Experimental results show that, compared with the traditional link prediction algorithm, the proposed combination algorithm can effectively dig out the group characteristics of social networks and improve the accuracy of college students’ association classification.

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