Complementing text embeddings with structural information

This study delves into various combinations of text and network embeddings, assessing their impact on the accuracy of graph machine learning tasks like node classification on the Cora citation dataset. The paper investigates methods to combine graph neural networks with straightforward text embeddings to boost quality in downstream graph machine learning tasks. The integration of both network representations and natural language processing methods is utilized to capture a broader array of information, thereby enhancing the efficiency of the models. Through this exploration, it is discovered that a fusion of these methods can significantly improve classification accuracy, offering a valuable reference point for researchers seeking to refine and optimize these models. The work is a continuation of our previous studies in the field, which laid a solid foundation for understanding and improving fusion models and graph machine learning tasks.