# NodeVector: A Novel Network Node Vectorization with Graph Analysis and Deep Learning
## 5 Conclusions  
In this paper, we propose that using network analysis instead of deriving sentences from networks will produce more effective results in order to develop a method of embedding nodes in datasets modeled with networks. This is the first study to combine network analysis with deep learning to produce a node representation with unsupervised learning.  
Figure 7: Evaluation of the \(F1\) score for competitor algorithms on \(S\). _cerevisiae_, BlogCatalog, and Wikipedia datasets. The \(y\)-axis represents the \(F1\) score, the \(x\)-axis represents competitor algorithms work on different network datasets.  
With the purpose of demonstrating that our algorithm transfers the information in the network data structure to node embeddings and performs better than its competitors, first, we define the NodeVector which is a novel node embedding algorithm for network data type. Then, we evaluate the performance of NodeVector with competitor state-of-the-art network node vectorization algorithms and we evaluate the relationship between the performance and negative sample percentage, initial node representation, random walk sampling, train epoch size, and different network datasets. In contrast to the majority of existing studies, we take advantage of network analysis, complex initial node representation, and balanced negative sampling. Our method consists of two phases. The first phase implements network enrichment using node similarities. The second one adapts deep neural networks to generate novel vector representations of each node. Experimental results confirm that our method has successful results and it is good in transferring the information in the network to node embeddings. Experimental results demonstrate the potential for collaboration between network analysis and deep machine learning to produce higher information node embeddings for use in classification problems. Due to the NodeVector algorithm we developed, more informative node embeddings are obtained. According to the experimental results, the use of network analysis, complex initial node representation, balanced negative sampling, and deep neural networks has a significant effect on the classification performance of node embedding. Therefore, techniques that make inferences from the target network through network analysis will have a higher success rate. Moreover, results are a brilliant indication that using network analysis is a potential candidate information extraction method for node embedding algorithms. These findings may lead to the investigation of subsequent novel studies for forthcoming research on network node embbedding techniques. It is a limitation that not all problems and all network analysis techniques can be examined. For this reason, network analysis can be diversified problem specifically and can have a positive impact on performance. In future studies, developing problem-specific network analysis methods for network data sources of different problems and finding integration methods with complex machine learning algorithms will enhance the progression of the developed methodology and increase the effectiveness of the outcomes.  
This research received no external funding.  
Not applicable.  
All data used in the study can be accessed from reference sources. Additionally, all data and source codes in the study can be accessed at nodevector.btu.edu.tr (accessed on 20 August 2023).  
The authors are grateful to TUBITAK ULAKBIM High Performance and Grid Computing Center (TRUBA resources), Bursa Technical University High-Performance Computing Laboratory.  
The authors declare no conflict of interest.