# NodeVector: A Novel Network Node Vectorization with Graph Analysis and Deep Learning
## 4 Results
### Evaluation  
Our main purpose in this study is to develop a more general node vectorization method for a network data structure that includes network analysis methods. In order to evaluate our method, first, the effect of negative sample percentage on the results is examined. In addition, the case of using all interactions instead of sampling with random walk from the dataset is examined. In order to evaluate the effect of the initial node representation method, we obtain the results by using both single label one-hot encoding and multi-label one-hot encoding on the dataset in which all interactions are used. In this experiment, we aim to show the effect of node vectorization phases on vector representation performance. To observe the effect of negative sampling ratio, we use 10, 5, 4, 3, 2, 1 and 0 as the ratio for negative sampling calculations. In order to evaluate the sampling effect with random walk, we use a separate dataset in which all interactions are used instead of sampling. We use the Homo Sapiens PPI as a network dataset with MSigDB Collections hallmark gene set labels. The purpose of this experiment is to evaluate the performance of our algorithm with the state-of-the-art approaches in the node classification task. We compare the \(F1\) score of our method with DeepWalk, LINE, Node2 Vec and Struct2 Vec. Parameters of competing algorithms are used as recommended in relevant reference articles. All algorithms use 128 as the node embedding size. For NodeVector algorithm, different values can be chosen for the \(k\) variable in the \(L_{k}\) adjacent similarity calculation. The networks used in the experiments in the literature have the small-world feature, and the neighborhood clusters formed at \(k>3\) values cover more than half of the target network. For this reason, we limit the \(k\) parameter of NodeVector to a maximum of three. We limit the candidate edge ratio \(\epsilon\) variable to two in \(ce_{adjsim}\) edge count calculation of the NodeVector algorithm. Figure 4 illustrates the results. As shown in Figure 4, the use of negative samples has a positive effect on all algorithms. This is an expected result due to the dataset balancing function. Although the role of negative sampling is critical, its impact is limited. Continuously increasing the negative sample rate does not ensure continuous improvement of the results for all algorithms. Figure 4: Evaluation of the \(F1\) score for competitor algorithms on Homo Sapiens. The negative sampling ratio varies from 10 to 5, 4, 3, 2, 1, and 0. OnlyInteractions: uses all interactions without sampling. SL: uses single-label one-hot encoding. ML uses multi-label one-hot encoding. The \(y\)-axis represents the \(F1\) score, the x-axis represents the applied algorithms, and the coloring represents the negative sampling ratio. However, the results of all algorithms decrease dramatically when negative samples are not used; the least affected algorithm is the NodeVector algorithm. Experiments using only all interactions have the lowest performances. Sampling with random walk has a positive effect on the results. The network enrichment and cluster node representation adopted by NodeVector have a more positive impact on the results. In all cases, the NodeVector algorithm produces better results. All algorithms have acceptable results on the problem. The method in which all interactions are used directly and nodes are represented independently with single-label one-hot encoding has the lowest performance results. On the other hand, in the same method, when the node representation is replaced with multi-label one-hot encoding to cover the cluster it is in, the results are lower than those its competitors, but increase to an acceptable performance level. This result shows the importance of initial node representation in the methods used.