# NodeVector: A Novel Network Node Vectorization with Graph Analysis and Deep Learning
## 2 Background  
Studies in network node embedding represent a diverse range of techniques and approaches for learning meaningful representations of nodes in networks. [PERSON] et al. [11] develop an unsupervised method, DeepWalk, for learning latent representations of vertices in a network. They draw inspiration from natural language processing and word embeddings to create meaningful representations for nodes in networks. The algorithm learns node representations by treating random walks in the network as sentences and applying skip-gram models. The algorithm creates node representations through unsupervised learning. To learn feature representations for nodes in a network, [PERSON] et al. [12] propose a framework, node2 vec, that maps nodes to a low-dimensional space of features. By employing a combination of breadth-first and depth-first search strategies during random walks, this algorithm allows for various neighborhoods around nodes to be transformed into paths (sentences) consisting of consecutive pairs of nodes. Then, the algorithm creates node representations through unsupervised learning using the generated sentences. For network embedding, LINE is an unsupervised technique developed by [PERSON] et al. [13]. It focuses on preserving network proximities and structures. This algorithm has a sampling strategy and an objective function that takes into account the probabilities of node pairs being connected in both first-order and second-order proximity. Then, the algorithm generates node representations via unsupervised learning from sampled sentences. To learn node representations in network data from structural identity, [PERSON] et al. [14] propose the Struc2 Vec algorithm. This approach considers not just the local neighborhood, but also the structural identity of nodes in a network. This algorithm is inspired by natural language processing. It leverages random walks to capture structural information about  
Figure 1: Sentence–graph–sentence derivation scenario. (**a**) Representation of the original sentences that existed at the beginning. (**b**) Representation of sentences with undirected network data structure. Articles and auxiliary verbs are deleted for fair comparison. (**c**) Sentences generated from the network. nodes in a network. The algorithm incorporates higher-order structural information by considering nodes with similar random walk patterns. The algorithm generates node representations via the process of unsupervised learning. Developed by [PERSON] et al. (2015), Graph Convolutional Networks (GCNs) generalize the concept of convolutional neural networks (CNNs) to handle irregular and non-Euclidean data structures such as graphs. GCNs operate by recursively aggregating information from neighboring nodes in a network, allowing for each node to update its feature representation based on its local connectivity patterns. This algorithm can create node representations through supervised learning using node labels. [PERSON] et al. (2016) propose GraphSAGE that learns embeddings for nodes by sampling and aggregating features from their neighbors. Instead of relying solely on the local neighborhood of nodes, GraphSAGE employs a sampling and aggregation framework. It samples and aggregates feature information from a node's neighbors. The algorithm can take node labels as input and produce new node embeds with the supervised learning method. Unsupervised techniques for network node embedding hold significant value within the field due to their capacity to discern meaningful node representations exclusively derived from the inherent topological structure of the network. This feature makes them particularly suitable in situations characterized by scarcity or complete absence of labeled data. It creates input for supervised learning techniques and enables the methods to produce more accurate results. All studies made significant contributions to the field, and they continue to be foundational for various applications in network analysis.