# NodeVector: A Novel Network Node Vectorization with Graph Analysis and Deep Learning
## 4 Results
### Evaluation
Contrary to the one-hot encoding method, which provides independent coding of words in natural language processing, the data in the network data structure can be represented as clusters with multi-label one-hot encoding due to its structure. Experimental results show that making the nodes independent causes information loss in the network, and the representation of the neighborhood patterns they create with the nodes around them contains a high amount of information. Unlike the text data type, using \(L_{1}\) level representation for the network data type increases representation performance. The method of generating sentences from the network by random walk, which is used by state-of-the-art algorithms in the literature, is a common method that has proven itself with a positive effect in many studies and forms the basis of existing methods. In this set of experiments, we examine the effect of random walk sample selection on NodeVector algorithm performance. Our method can produce repetitive edges during mesh enhancement. To sample repeated edges with random walk, we use the number of repeated edges between two nodes as the weight of the edge between two nodes. With random walk, we take into account all the weights in each node when choosing the direction. To create a balanced dataset, we use as many negative examples as positive examples. Another parameter that can affect the performance of our method is the number of candidate edges used in network enrichment. This experiment evaluates the ways in which the number of candidate edges affects performance. Different values can be selected for the \(\epsilon\) variable in \(cc_{adjsim}\) edge count calculation. We use two, three, four, five as the candidate edge ratio, \(\epsilon\). Figure 5 illustrates the results. Figure 5: Evaluation of the effect of random walk sampling and candidate edge ratio on the NodeVector algorithm. The candidate edge ratio varies from 2, 3, 4, to 5. The \(y\)-axis represents the \(F1\) score, the \(x\)-axis represents the NodeVector algorithm with/without sampling, and the coloring represents the candidate edge ratio. According to the experimental results, random walk sampling provides a positive effect on the results of the NodeVector algorithm, as in similar studies in the literature. Although random walk provides a positive effect, the performance increase over the NodeVector algorithm remains limited. As shown in Figure 5, the two percent network enhancement edge addition rate parameter has the highest performance values. Increasing the edge ratio beyond three percent of the number of available edges adversely affects the results. The basis of the node vectorization method that we propose in this study is the embedding of multidimensional representation data into lower-dimensional vector representations with the help of deep artificial neural networks. Since training neural networks requires time and processing power, a long training phase can be a disadvantage. The parameter denoting the number of epochs in the context of gradient descent serves as a hyperparameter, exerting influence over the complete traversal of the training dataset. In this set of experiments, we examine the effect of the training repetition parameter epoch on the results. We use values 2, 4, 6, 8, 10, 20, 50, 100 as the epoch parameter. Figure 6 illustrates the results. According to experimental results, low-dimensional vector representations cannot be learned fully at epoch numbers below eight and have a negative impact on the results. Vector representations created with epoch numbers of 10 and above have maximum efficiency. Increasing the epoch number above 10 does not increase performance. In vector representation learning, it is sufficient to use 10 as the epoch number. The results obtained are compatible with the literature.