Venue matching in social network APIs using neural networks

2018 ◽  
Author(s):  
Vassilios Kalavrouziotis ◽  
Andreas Komninos ◽  
John Garofalakis
Keyword(s):  
Neural Networks ◽  
Social Network

scholarly journals A Learning Social Network with Recognition of Learning Styles Using Neural Networks

2010 ◽  
pp. 199-209 ◽  
Author(s):  
Ramón Zatarain-Cabada ◽  
M. L. Barrón-Estrada ◽  
Viridiana Ponce Angulo ◽  
Adán José García ◽  
Carlos A. Reyes García
Keyword(s):  
Neural Networks ◽  
Social Network ◽  
Learning Styles

scholarly journals Use of neural networks for forecasting of the exposure of social network users to destructive impacts

2020 ◽  
pp. 24-33
Author(s):  
Elena Doynikova ◽  
Aleksandr Branitskiy ◽  
Igor Kotenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Social Networks ◽  
Artificial Neural Networks ◽  
Social Network ◽  
Negative Impact ◽  
Young Generation ◽  
The Neural Network ◽  
Internet Environment ◽  
Artificial Neural

Introduction: In social networks, the users can remotely communicate, express themselves, and search for people with similarinterests. At the same time, social networks as a source of information can have a negative impact on the behavior and thinking oftheir users. Purpose: Developing a technique of forecasting the exposure of social network users to destructive influences, based onthe use of artificial neural networks. Results: A technique has been developed and experimentally evaluated for forecasting Ammon’stest results by a social network user’s profile using artificial neural networks. The technique is based on the results of Ammon’s testfor medical students. For training the neural network, a set of features was generated based on the information provided by socialnetwork users. The results of the experiments have confirmed the dependence between the data provided by social network users andtheir psychological characteristics. A mechanism has been developed aimed at prompt detection of destructive impacts or social networkusers’ profiles indicating the susceptibility to such impacts, in order to facilitate the work of psychologists. The experiments haveshown that out of the four investigated types of neural networks, the highest accuracy is provided by a multilayer neural network. Inthe future, it is planned to expand the set of features in order to achieve a better accuracy. Practical relevance: The obtained results canbe used to develop systems for monitoring the Internet environment, detecting the impacts potentially dangerous for mental health ofthe young generation and the nation as a whole.

scholarly journals Implementing M-Learning System for Learning Mathematics Through Computer Games and Applying Neural Networks for Content Similarity Analysis of an Integrated Social Network

2021 ◽  
Vol 15 (13) ◽  
pp. 145
Author(s):  
Petar Juric ◽  
Marija Brkic Bakaric ◽  
Maja Matetic
Keyword(s):  
Neural Networks ◽  
Social Network ◽  
Computer Games ◽  
Primary Schools ◽  
Learning System ◽  
Learning Mathematics ◽  
Educational Computer ◽  
E Learning ◽  
Content Similarity ◽  
Educational Computer Games

In order to make e-learning systems more readily available for use, the majority of new systems are being developed in a form suitable for mobile learning, i.e. m-learning. The paper puts focus on the parts of the implementation of an e-learning system which is not restricted to desktop platforms, but works equally well on smartphones and tablets in the form of m-learning. The implemented system uses educational computer games for learning Mathematics in primary schools and has an integrated social network, which is used for communication and publishing of the content related to the game. Besides analysing the platforms used for accessing the system (desktop/mobile), since students are given a choice, the paper also questions how to interpret messages when they contain concepts in student jargon or generally unknown to teachers, and shows that these messages can be interpreted by applying neural networks.

Learning ladder neural networks for semi-supervised node classification in social network

2021 ◽  
Vol 165 ◽  
pp. 113957
Author(s):  
Bentian Li ◽  
Dechang Pi ◽  
Yunxia Lin
Keyword(s):  
Neural Networks ◽  
Social Network ◽  
Node Classification

scholarly journals Deep Active Learning for Anchor User Prediction

2019 ◽  
Author(s):  
Anfeng Cheng ◽  
Chuan Zhou ◽  
Hong Yang ◽  
Jia Wu ◽  
Lei Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Social Network ◽  
Active Learning ◽  
Prediction Models ◽  
State Of The Art ◽  
Network Data ◽  
Multiple Networks ◽  
Social Network Data ◽  
Model Training ◽  
Vector Representations

Predicting pairs of anchor users plays an important role in the cross-network analysis. Due to the expensive costs of labeling anchor users for training prediction models, we consider in this paper the problem of minimizing the number of user pairs across multiple networks for labeling as to improve the accuracy of the prediction. To this end, we present a deep active learning model for anchor user prediction (DALAUP for short). However, active learning for anchor user sampling meets the challenges of non-i.i.d. user pair data caused by network structures and the correlation among anchor or non-anchor user pairs. To solve the challenges, DALAUP uses a couple of neural networks with shared-parameter to obtain the vector representations of user pairs, and ensembles three query strategies to select the most informative user pairs for labeling and model training. Experiments on real-world social network data demonstrate that DALAUP outperforms the state-of-the-art approaches.

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