@article{article_1704305, title={LG-GNNs: Enhancing Node Classification via Line Graph Transformations for Higher-Order Structural Learning}, journal={Turkish Journal of Engineering}, volume={9}, pages={670–677}, year={2025}, DOI={10.31127/tuje.1704305}, author={Qarkaxhija, Jusuf and Melekoglu, Engin and Gök, Murat}, keywords={Graph Neural Networks, Graph Transformation, Node Classification, Higher-Order Learning}, abstract={Graph Neural Networks (GNNs) have demonstrated remarkable success in learning robust representations from complex graph-structured data. However, standard GNNs architectures often fail to capture higher-order structural information effectively, resulting in suboptimal performance on tasks that require a nuanced understanding of intricate node dependencies. In this work, we present a novel architectural innovation—the Line Graph GNN (LG-GNNs)—which integrates the line graph transformation into the traditional GNNs framework. By explicitly modeling higher-order interactions among edges, our approach enriches node embeddings with additional structural context. We evaluate LG-GNNs on a variety of benchmark datasets that capture both simple and complex network structures, comparing its performance against conventional GNNs architectures. Experimental results demonstrate that LG-GNNs consistently outperforms baseline models in terms of classification accuracy while maintaining competitive efficiency. Motivated by the shortcomings of standard GNNs in capturing complex relational dependencies in real-world networks, our method delivers a more expressive representation that enhances predictive performance across diverse applications. To encourage reproducibility and further research in the field, we have made our implementation and experimental results publicly available.}, number={4}, publisher={Murat YAKAR}