## 3 Model and Dataset
### Data  
The publication data for this study were obtained from two sources: 347 RF-EMF publications from the open EMF portal database [2], and an additional 108 publications provided by the Electronics and Telecommunications Research Institute in Korea. These publications underwent expert review and labeling, resulting in an initial dataset of 455 papers. The EMF portal dataset was collected from four categories (cells, brain [cognitive function], brain metabolism, and DNA), and any unclassifiable data were excluded. The last access date for the EMF portal was on 22 September 2021. Subsequently, human annotators manually reviewed the abstracts and labeled them into three classes (in vivo, in vitro, and other). To construct a citation graph, it was necessary to collect citation data between publications. PubMed's unique ID system was used to identify citation relationships between papers. The publications that were not registered in PubMed and any duplicate data were excluded from the dataset. Additionally, publications without citation information in PubMed were also removed. as a result, a total of 396 papers were identified as nodes in the citation graph. Figure 1: Three modes of GCN-based document classification. (**a**) Statistical GCN (BertGCN). The multiplier lambda adjusts the trade-off between the two objectives, where \(\lambda=1\) corresponds to the full BertGCN model and \(\lambda=0\) corresponds to using only the BERT module [19]; (**b**) Statistical GCN (BertGCN) without \(\lambda\). Concatenating the output of both models replaces the mixture process with linear layers, which eliminates the need for the mixing parameter \(\lambda\) and simplifies the neural network architecture; (**c**) citation-based GCN. This model constructs a citation graph using the 396 papers and their corresponding citation papers. The openly published PubMed database served as an essential resource for collecting citation data for our research. An additional set of 1011 papers was collected by tracing citations from the original 396 papers, and the entire dataset was used to create nodes in the graph neural network. Accessing abstracts of the cited papers was achieved by filtering PubMed IDs from the metadata of each paper. The citation graph (citation-based GCN) was constructed using a total of 1407 paper abstracts as nodes, while the statistical graph (statistical GCN) incorporated both paper abstracts and words as nodes. A total of 1407 paper abstracts were classified into three groups: (a) in vivo (\(n=523\)), (b) in vitro (\(n=332\)), and (c) Other (\(n=552\)). During the data selection process of the 1011 cited papers, two primary criteria were used. Firstly, this study focused specifically on RF-EMF generated from electronic devices, such as mobile phones and laptops, and excluded radiation sources that are distinct from RF-EMF, including gamma rays. Secondly, papers on ionizing radiation sources were also excluded, as RF-EMF from electronic devices is classified as a non-ionizing radiation source. The selection criteria included microwave radiation and short-wavelength visible light. Notably, the following types of light sources were excluded from the study:  
* Gamma rays;
* Pulsed electric fields (either electric fields or pulse);
* Only magnetic field (MF);
* Ionizing radiation (e.g., brain CT scans leading to Alzheimer's and Parkinson's);
* X-rays. Document nodes represent abstracts of the papers, and the word nodes represent all words used in the entire data set. Word nodes have statistical associations with other words and documents [10]. Both BertGCN and citation-based GCN utilized 1407 documents. However, BertGCN used 1407 document nodes and 33,115-word nodes together, whereas the citation-based GCN only used 1407 document nodes. BertGCN employed the transductive classification method (semi-supervised), which treated word nodes as unlabeled data [20,21]. In summary, the citation graph comprises 1407 nodes, consisting of 396 primary papers and their 1011 cited papers.