# NodeVector: A Novel Network Node Vectorization with Graph Analysis and Deep Learning
###### Abstract  
Network node embedding captures structural and relational information of nodes in the network and allows for us to use machine learning algorithms for various prediction tasks on network data that have an inherently complex and disordered structure. Network node embedding should preserve as much information as possible about important network properties where information is stored, such as network structure and node properties, while representing nodes as numerical vectors in a lower-dimensional space than the original higher dimensional space. Superior node embedding algorithms are a powerful tool for machine learning with effective and efficient node representation. Recent research in representation learning has led to significant advances in automating features through unsupervised learning, inspired by advances in natural language processing. Here, we seek to improve the representation quality of node embeddings with a new node vectorization technique that uses network analysis to overcome network-based information loss. In this study, we introduce the NodeVector algorithm, which combines network analysis and neural networks to transfer information from the target network to node embedding. As a proof of concept, our experiments performed on different categories of network datasets showed that our method achieves better results than its competitors for target networks. This is the first study to produce node representation by unsupervised learning using the combination of network analysis and neural networks to consider network data structure. Based on experimental results, the use of network analysis, complex initial node representation, balanced negative sampling, and neural networks has a positive effect on the representation quality of network node embedding. node embeddings; network analysis; unsupervised learning; node classification; graph representations; pattern recognition +
Footnote †: journal: Medical  
designed based on their relevance and ability to capture meaningful information. Effective features capture relevant patterns, trends, or properties that help in solving specific tasks, such as classification, clustering, or regression [6]. Whether it is identifying important variables in a dataset, extracting key characteristics from images or text, or describing the distinguishing attributes of a product or service, features are essential for making sense of complex information and enabling effective decision-making. Feature engineering is a critical process in machine learning and data analysis, involving the creation, transformation, and selection of input features to improve the performance and effectiveness of predictive models. It aims to extract relevant information from raw data and represent it in a form that facilitates the learning process and enhances the model's ability to capture patterns and make accurate predictions [7]. Feature engineering involves several techniques, including feature extraction, where new features are derived from existing ones, feature transformation, which involves applying mathematical or statistical operations to the features, and feature selection, which involves identifying the most informative and discriminative features for the task at hand. By carefully crafting and refining features, practitioners can reduce noise, highlight important patterns, handle missing data, and ensure that the model has access to the most relevant and meaningful information [8]. Effective feature engineering requires a deep understanding of the problem domain, the available data, and the underlying relationships, and it can significantly impact the performance and interpretability of machine learning models. Unsupervised learning is a branch of machine learning that deals with the analysis of data without explicit labels or target variables. Unlike supervised learning, where the model is trained on labeled data to make predictions or classifications, unsupervised learning focuses on finding patterns, structures, and relationships within the data themselves [9].