Network alignment across social networks using multiple embedding techniques

Network alignment, which is also known as user identity linkage, is a kind of network analysis task that predicts overlapping users between two different social networks. This research direction has attracted much attention from the research community, and it is considered to be one of the most important research directions in the field of social network analysis. There are many different models for finding users that overlap between two networks, but most of these models use separate and different techniques to solve prediction problems, with very little work that has combined them. In this paper, we propose a method that combines different embedding techniques to solve the network alignment problem. Each association network alignment technique has its advantages and disadvantages, so combining them together will take full advantage and can overcome those disadvantages. Our model combines three-level embedding techniques of text-based user attributes, a graph attention network, a graph-drawing embedding technique, and fuzzy c-mean clustering to embed each piece of network information into a low-dimensional representation. We then project them into a common space by using canonical correlation analysis and compute the similarity matrix between them to make predictions. We tested our network alignment model on two real-life datasets, and the experimental results showed that our method can considerably improve the accuracy by about 10-15% compared to the baseline models. In addition, when experimenting with different ratios of training data, our proposed model could also handle the over-fitting problem effectively.Web of Science1021art. no. 397

Constructing a cryptocurrency-price prediction model using deep learning

The purpose of this study is to discover the optimal Deep Learning model for Bitcoin prediction among the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). Our empirical results indicate that LSTM is the optimal model for predicting Bitcoin price and trend with the prediction accuracy of 88.9%. Our study serves as a stepping stone for novice cryptocurrency investors and future studies of more advanced and sophisticated algorithms. Finally, given that the ideal model for predicting the price of cryptocurrencies is still a topic of controversy, the findings of this study will serve as a valuable empirical resource for future studies. © 2022 IEEE

ARIMA for short-term and LSTM for long-term in daily Bitcoin price prediction

The goal of this paper is the insight into the forecasting of Bitcoin price using machine learning models like AutoRegressive Integrated Moving Average (ARIMA), Support vector machines (SVM), hybrid ARIMA-SVM, and Long short-term memory (LSTM). Depending on the different types of data and the period, various models are used for prediction. A single model may be the best fit in the short term but may not be the best in long-term series data. Thus, using only a single model may not be suitable for forecasting time series data that depends on data sampling length and prediction time, and the type of specific applications. As a result, the ARIMA model produces better error results with a short prediction period or a small data set. In contrast, the Hybrid ARIMA-SVM model will help improve the performance of the ARIMA model when predicting over a long period, specifically 7 and 30 days for Bitcoin price prediction used in this research paper. The paper aims to compare traditional models such as the ARIMA, the Hybrid ARIMA-SVM, and deep learning models such as LSTM on a specific cryptocurrency prediction task using different scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.IGA/CebiaTech/2022/001; Vysoká Škola Bánská - Technická Univerzita Ostrav