# Edge Convolutional Networks for Style Change Detection in Arabic Multi-Authored Text
## 1 Introduction  
With the rapid development of Artificial Intelligence technologies and the availability of textual data, the demand has increased to differentiate between the styles of authors who collaboratively write a document. A style change detection (SCD) task focuses on finding the positions of writing style changes in multi-authored documents [1, 2, 3, 4, 5, 6, 7]. SCD plays a pivotal role in many practical applications. In the authorship attribution task, detecting positions of style changes can help segment a document before mapping each segment to its author [8]. In legal contexts, the demand for forensic linguistic and law enforcement applications motivates work on this task [8, 9]. Analyzing linguistic cues of law documents, such as threatening letters or ransom notes, can provide valuable insights into the potential style changes. In security measures, analyzing writing styles and detecting changes in sensitive documents can help find unauthorized modifications. In plagiarism, SCD-based solutions can suggest potential plagiarism cases by identifying changes in writing style without comparing the suspected and source documents. In historical documents, such solutions can help to identify literary plagiarism [10]. In commerce, SCD-based solutionscan help evaluate the consistency of writing assistance tools and guide proofreaders to improve the coherence of overall texts with the fewest possible breaks in the flow of the texts [8]. SCD-based solutions can also help institutions adhere to a specific style in their documents, maintaining credibility. The SCD task is related to a particular language. For the English language, the annual organization of PAN competitions ([[https://pan.webis.de](https://pan.webis.de)]([https://pan.webis.de](https://pan.webis.de)), accessed on 30 April 2025) has become an essential event for encouraging researchers to develop state-of-the-art (SOTA) solutions on English datasets [1; 2; 3; 4; 5; 6; 7; 11]. To the best of our knowledge, there is no available SCD solution for the Arabic language. Since 2020, most SCD solutions have detected the style changes based on pretrained models to embed inputs and Fully Connected (FC) layers for classification [4; 5; 6; 7; 11]. This approach represents any boundary surrounding two segments by concatenating these segments as a single input and subsequently classifies it. However, this approach may lose the contextual information and distinct style patterns presented within each segment. The concatenation may also lead to potential confusion in style boundaries, where one style ends and another begins. Concatenating two segments results in an input with a longer length, while most pretrained models restrict the input lengths. Therefore, another approach can be investigated to preserve the boundaries of each segment. In addition, comprehending relationships between textual segments, such as words and sentences, would enhance the detection of writing styles. Graph-based solutions take a graph as input, trying to involve structural properties within the data. Graph Neural Networks (GNNs) extend the existing neural networks to operate on graph-structured data directly [12]. Recently, GNN models have achieved promising results for some Natural Language Processing (NLP) tasks, such as an authorship verification task that determines whether an unknown text was written by a specific author [13; 14; 15] and semantic relationship tasks that analyze semantic relations between textual segments [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. To the best of our knowledge, no existing GNN-based solution is available for the SCD task. In this paper, we introduce the Edge Convolutional Neural Network for the Arabic SCD task (ECNN-ASCD) based on GNNs to detect writing style changes in Arabic texts. ECNN-ASCD is considered the first solution to tackle the SCD task in Arabic texts to the best of our knowledge. It was trained particularly to learn the characteristics of Arabic writing styles.