## 2 Related Works  
We hypothesized that certain types of relationships between scientific publications could influence classification performance, as previously discussed in our work [2]. Since citations are a well-known form of interrelations between papers, we explored the application of citation graphs and citation analysis to NLP, specifically in document classification [11; 12]. Since the early and mid-2000s, several studies [5; 6; 13] have shown that analyzing relationships between publications, such as citation information, can significantly improve the classification performance of scientific literature, rather than solely focusing on each isolated literature. With the study of [PERSON] and [PERSON] (1990), Infometrics defined the citation graph as a representation of a document \(\text{d}_{i}\) that cites a document \(\text{d}_{j}\) through an arrow from node \(\text{d}_{i}\) to \(\text{d}_{j}\)[14]. By extending this notation to all documents in a collection, a directed citation graph can be constructed. Several studies have employed machine learning techniques for document classification using citation graph structures. For instance, [PERSON] and [PERSON] (2017) applied Latent Dirichlet allocation (LDA) on a large-scale citation network [7]. [PERSON] et al., in one of the earliest studies on document classification that leveraged the relations among nodes in a citation graph, proposed an efficient kernel method based on the Support Vector Machine (SVM) [6]. The kernel method is a learning algorithm that relies on a similarity value, which is represented as the kernel values. To improve the representation of the citation network, they utilized graph kernels, which outperformed the contemporary model of simply directed citations by capturing the richer features of the citation network context. Co-citations in citation networks have also been studied by several researchers [5; 15]. For example, [PERSON] et al. (2010) developed a Naive Bayes classifier using features extracted from the network [5]. Their study is noteworthy as it achieved encouraging performance and focused on a research topic similar to our own. The study also used PubMed publication data for classifying biomedical literature. However, applying their method to our research is challenging as their model is limited to binary classification, whereas our work aims to classify at least three groups. A number of studies have employed deep neural networks (DNN) and reported impressive performance. Notably, some approaches involve developing DNN models with a graph structure. This concept has its roots in the pioneering work by [PERSON] and [PERSON] (2017) [4], who applied the classical image convolution concept to datasets with graph structures such as citation relationships. They developed an improved Graph Convolution Neural Network (GCN) model, which has paved the way for various follow-up studies by overcoming the limitations of classical spatial convolution. Spatial convolution in graph networks faced two critical issues: exploding similarity of nodes and edge information loss. To address the issues primarily caused by the Laplacian regularizer in spatial convolution, they employed Fourier transformation to extract one of the candidate graphs with the lowest node similarities. This novel convolution, based on such a spectral perspective, achieved excellent performance in node classification tasks. After [PERSON] and [PERSON] introduced the GCN model, researchers made various attempts to apply it to NLP tasks. Among these, TextGCN has been the most successful [10; 16]. The authors of TextGCN, [PERSON] et al. (2018), proposed a method to improve document classification in NLP using GCN. Their study specifies how to construct node features consisting of all the words and documents and how to relate those nodes to build edges and their corresponding weight values.