# NodeVector: A Novel Network Node Vectorization with Graph Analysis and Deep Learning
## 4 Results
### Evaluation
With the network enrichment used in the NodeVector algorithm, increasing the number of edges in the network and the initial representation of the nodes with a multi-label one-hot vector instead of a one-hot vector does not create an additional load on deep artificial neural network training. The training cost is the same as that of competing algorithms. Although our study mainly focuses on node function predictions in protein interaction networks, which are a multi-label node classification problem, the method we develop is designed to work in all network data structures, regardless of the problem. In this set of experiments, we test the performance of our method with different network datasets available in the literature. We use datasets _S. cerevisiae_, BlogCatalog, and Wikipedia in experimental studies. The parameters used in our method are the parameters obtained in  
Figure 6: Evaluation of the effect of the training repetition parameter epoch on the NodeVector algorithm. The epoch parameter varies from 2, 4, 6, 8, 10, 20, 50 to 100. The \(y\)-axis represents the \(F1\) score, the \(x\)-axis represents the Node Vector algorithm for various training repetition parameters. previous experimental datasets, and they belong to the best performances. Likewise, it is used with the parameters recommended in state-of-the-art methods in the literature. As can be seen from previous experimental results, comparisons are fair since all techniques offer good results with similar parameters. Figure 7 illustrates the results. It can be seen in the experimental results in Figure 7 that our NodeVector algorithm has a positive effect on all datasets. NodeVector has the highest \(F1\) score. Struct2 Vec has the lowest \(F1\) score. All algorithms have acceptable results on all datasets. Algorithms based on sampling with random walk have similar performance. NodeVector can be applied to all problems that can be expressed with the graph data type. Regardless of the problem, NodeVector can transfer the information in the network to the node representation with the help of network analysis and embedding. According to all experimental results, our NodeVector algorithm has successful results. \(L_{1}\) neighborhood representation, which incorporates the interactions of each node in the network, contains a high amount of information and contributes positively to performance. Detection of undetected potential edges by network analysis and network enrichment by adding repetitive edges that produce positive results in different analyzes to the network increases the node representation quality and improves the results. Although random walk sampling has a positive effect, it is limited. Although using negative samples to create a balanced dataset has a positive effect, there is no need to use a high rate of negative samples. Low-level node representations created by unsupervised learning with the help of skip-gram and deep artificial neural network are applicable to all problems stored in the network data structure. Unlike the text data type, since the information contained in the sentences is not available in the network data type, it is beneficial to use network analyzes to preserve the information contained in the network data instead of reproducing sentences. For the network data type based on network analysis, our generalized NodeVector algorithm has results at least as good as its alternatives. Our method of creating node representation using existing information in the network may lead to novel network analysis development work in the future.