A Complex Neural Network Model for Predicting a Personal Success based on their Activity in Social Networks

Gafarov, Fail; Nikolaev, Konstantin Sergeevich; Ustin, Pavel N.; Berdnikov, Andrey Anatolyevich; Zakharova, Valeria L.; Reznichenko, S. A.

doi:10.29333/ejmste/11175

Cited by 7 publications

(10 citation statements)

References 24 publications

(18 reference statements)

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“…This social network has a convenient API-VK interface that allows researchers to download data directly from VKontakte database. Based on data obtained from VKontakte, we had previously developed simple multilayer feed-forward neural models for predicting the professional success of users of this social network [26]. In the present work we developed a graph neural network-based model (by using a labelled dataset introduced in [26]), and by using these models we significantly improved the classification accuracy (from 0.77 to 0.88 for a 5-parameter dataset [26]).…”

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confidence: 82%

“…Based on data obtained from VKontakte, we had previously developed simple multilayer feed-forward neural models for predicting the professional success of users of this social network [26]. In the present work we developed a graph neural network-based model (by using a labelled dataset introduced in [26]), and by using these models we significantly improved the classification accuracy (from 0.77 to 0.88 for a 5-parameter dataset [26]). For each user and his local neighbourhood connectivity (friends and friends of friends) quantitative metrics were downloaded by using API-VK, and local connectivity graphs were built.…”

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confidence: 82%

“…Social networks analysis (SNA) is also used in recruitment to identify potentially successful future employees [23] [24] or to recommend suitable vacancies [25]. In our previous work, we built a model based on the use of feed-forward neural networks to predict professional success on the basis of data from user profiles in VKontakte social network [26].…”

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confidence: 99%

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Online social network user performance prediction by graph neural networks

Gafarov

Berdnikov²,

Ustin³

2022

Int. J. Adv. Intell. Informatics

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Online social networks provide rich information that characterizes the user’s personality, his interests, hobbies, and reflects his current state. Users of social networks publish photos, posts, videos, audio, etc. every day. Online social networks (OSN) open up a wide range of research opportunities for scientists. Much research conducted in recent years using graph neural networks (GNN) has shown their advantages over conventional deep learning. In particular, the use of graph neural networks for online social network analysis seems to be the most suitable. In this article we studied the use of graph convolutional neural networks with different convolution layers (GCNConv, SAGEConv, GraphConv, GATConv, TransformerConv, GINConv) for predicting the user’s professional success in VKontakte online social network, based on data obtained from his profiles. We have used various parameters obtained from users’ personal pages in VKontakte social network (the number of friends, subscribers, interesting pages, etc.) as their features for determining the professional success, as well as networks (graphs) reflecting connections between users (followers/ friends). In this work we performed graph classification by using graph convolutional neural networks (with different types of convolution layers). The best accuracy of the graph convolutional neural network (0.88) was achieved by using the graph isomorphism network (GIN) layer. The results, obtained in this work, will serve for further studies of social success, based on metrics of personal profiles of OSN users and social graphs using neural network methods.

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confidence: 82%

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confidence: 82%

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Online social network user performance prediction by graph neural networks

Gafarov

Berdnikov²,

Ustin³

2022

Int. J. Adv. Intell. Informatics

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“…Because of its simple structure and outstanding nonlinear function approximation capacity, the RBFNN is often used, especially in data classification and nonlinear system modeling ( Yang et al, 2013 ; Han et al, 2015 ; Xiong et al, 2015 ; Xu et al, 2016 ). When the nature of relationship between research variables is complex, nonlinear, and difficult to specify in structural equations, NNs may be utilized as statistical tools for data processing and may even outperform standard statistical processes in classification and completion tasks ( Garson, 1998 ; Levine, 1998 ; Gafarov et al, 2021 ).…”

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confidence: 99%

A Radial Basis Function Neural Network Approach to Predict Preschool Teachers’ Technology Acceptance Behavior

et al. 2022

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With the continual development of artificial intelligence and smart computing in recent years, quantitative approaches have become increasingly popular as an efficient modeling tool as they do not necessitate complicated mathematical models. Many nations have taken steps, such as transitioning to online schooling, to decrease the harm caused by coronaviruses. Inspired by the demand for technology in early education, the present research uses a radial basis function (RBF) neural network (NN) modeling technique to predict preschool instructors’ technology usage in classes based on recognized determinant characteristics of technology acceptance. In this regard, this study utilized the RBFNN approach to predict preschool teachers’ technology acceptance behavior, based on the theory of planned behavior, which states that behavioral achievement, in our case the actual technology use in class, depends on motivation, intention and ability, and behavioral control. Thus, this research design is based on an adapted version of the technology acceptance model (TAM) with eight dimensions: D1. Perceived usefulness, D2. Perceived ease of use, D3. Perceived enjoyment, D4. Intention to use, D5. Actual use, D6. Compatibility, D7. Attitude, and D8. Self-efficacy. According to the TAM, actual usage is significantly predicted by the other seven dimensions used in this research. Instead of using the classical multiple linear regression statistical processing of data, we opted for a NN based on the RBF approach to predict the actual usage behavior. This study included 182 preschool teachers who were randomly chosen from a project-based national preschool teacher training program and who responded to our online questionnaire. After designing the RBF function with the actual usage as an output variable and the other seven dimensions as input variables, in the model summary, we obtained in the training sample a sum of squares error of 37.5 and a percent of incorrect predictions of 43.3%. In the testing sample, we obtained a sum of squares error of 14.88 and a percent of incorrect predictions of 37%. Thus, we can conclude that 63% of the classified data are correctly assigned to the models’ dependent variable, i.e., actual technology use, which is a significant rate of correct predictions in the testing sample. This high significant percentage of correct classification represents an important result, mainly because this is the first study to apply RBFNN’s prediction on psychological data, opening up a new interdisciplinary field of research.

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“…We appreciate the scholarly excellence of the articles accepted for publication and congratulate the authors. Special issue starts with the article by Gafarov et al entitled "A Complex Neural Network Model for Predicting a Personal Success based on their Activity in Social Networks" (Gafarov et al, 2021), which presents a comprehensive model of success based on artificial neural networks and an analysis of qualitative and quantitative data. This tool is proposed to be used by psychologists in educational, professional, and other areas in the formation of trajectories of harmonious person's development.…”

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confidence: 99%