## 2 Related Works  
This section briefly reviews some earlier works that took place in the area of text classification tasks in general as well as some performing text classification by graph neural networks and their various types. Generally speaking, text classification tasks can be categorized into three main groups: First, _traditional text classification_ methods; second, _deep learning_ methods; and third, _graph neural networks_. In the following paragraphs, we present a summarized review of each one. First, text classification dates back to the early 1960s and by was conducted by domain experts. It required specialized knowledge about how to classify textual data into predefined categories. Machine learning approaches remained popular from the late 1980s until the early 1990s.The prevailing approach during this time for automatic text classification was knowledge engineering, which relied upon logical rules manually defined for text classification and eventually became the dominant approach. These methods primarily focused on the engineering of features and the algorithms used for classification. One commonly used technique for feature engineering was the bag-of-words [8] approach, wherein each word in the data was assigned a vector representation. These vectors served as inputs for machine learning algorithms such as logistic regression. Deep learning approaches for text classification, as one of the essential areas of application of deep learning, has gained incredible popularity in various NLP problems. This framework mainly concerns applying recurrent neural networks (RNNs) [9; 10; 11; 12], long short-term memory (LSTM) networks [13; 14], and transformer architectures for the analysis and classification of texts. RNNs are capable of preserving internal states or memory to retain information from previous inputs. This feature allows them to understand and discover relationships between words in long sequences. Having said that, RNNs have certain weaknesses (such as weak long-term dependency). LSTM has been proposed as a solution to address these weaknesses and is commonly used for sentiment analysis, language translation, and many other natural language processing tasks, as it is capable of processing sequential data of text and capturing long-term dependencies in it. Contrary to RNNs and LSTM, a newer and stronger architecture, namely transformers, utilizes an attention mechanism that allows for bidirectional and non-local relationships between data elements. Transformers give rise to a high capability to model long-range dependencies between words and sequence elements. Graph neural networks for text classification is the third and most important approach that we survey in this introduction and will be our main concern in the rest of the paper. In fact, one major advancement in the theory of neural networks is utilizing neural networks that can capture graph-structured data. One of the most important graph neural networks used for text classification is the graph convolutional network (GCN) [15]. The GCN generalizes the concept of convolutional neural networks (CNNs) [16] to graphs; it performs local aggregation operations on the neighbors of each node in the graph. Another algorithm is GraphSAGE [17], which employs node sampling and aggregation to create representations. This can be highly beneficial in problems that involve large graph generation. The graph attention network (GAT) [18] algorithm indeed uses the attention mechanism, which means that, during the message-passing process, it learns how to apply different weights to the neighbors of each node, meaning it assigns a different importance to each neighbor. This helps the GAT to capture complex relationships and dependencies within the graph, leading to improved performance in various graph-based tasks. The graph isomorphism network (GIN) [19] builds upon the Weisfeiler-Lehman (WL) graph isomorphism test, a widely used algorithm for distinguishing between graphs based upon their structural properties.