Textual data in graphs
One of the fundamental hurdles in deploying GNNs lies in acquiring sophisticated feature representations for nodes and edges. This becomes particularly crucial when these elements are associated with complex textual attributes such as descriptions, titles, or abstracts.
Traditional methods, such as the bag-of-words approach or the utilization of pre-trained word embedding models, have been the norm. However, these techniques typically fall short of grasping the subtle semantic intricacies inherent in the text. They tend to overlook the context and the interdependencies between words, leading to a loss of critical information that could be essential for the GNN to perform optimally.
To overcome this challenge, there’s a growing need for more advanced methods that can understand and encode the richness of language into the graph structure. This is where LLMs come into play. With their deep understanding of language nuances and context, LLMs can generate...
