GraFusionNet: Integrating Node, Edge, and Semantic Features for Enhanced Graph Representations

Understanding complex graph-structured data is a cornerstone of modern research in fields like cheminformatics and bioinformatics, where molecules and biological systems are naturally represented as graphs. However, traditional graph neural networks (GNNs) often fall short by focusing mainly on node features while overlooking the rich information encoded in edges. To bridge this gap, we present GraFusionNet, a framework designed to integrate node, edge, and molecular-level semantic features for enhanced graph classification. By employing a dual-graph autoencoder, GraFusionNet transforms edges into nodes via a line graph conversion, enabling it to capture intricate relationships within the graph structure. Additionally, the incorporation of Chem-BERT embeddings introduces semantic molecular insights, creating a comprehensive feature representation that combines structural and contextual information. Our experiments on benchmark datasets, such as Tox21 and HIV, highlight GraFusionNets superior performance in tasks like toxicity prediction, significantly surpassing traditional models. By providing a holistic approach to graph data analysis, GraFusion-Net sets a new standard in leveraging multi-dimensional features for complex predictive tasks. CCS CONCEPTSO_LIComputing methodologies [->] Neural networks. C_LI ACM Reference FormatMd Toki Tahmid, Tanjeem Azwad Zaman, and Mohammad Saifur Rahman. 2018. GraFusionNet: Integrating Node, Edge, and Semantic Features for Enhanced Graph Representations. In Proceedings of Make sure to enter the correct conference title from your rights confirmation email (Conference acronym XX). ACM, New York, NY, USA, 9 pages. https://doi.org/XXXXXXX.XXXXXXX