Bipartite Graph Coarsening for Text Classification Using Graph Neural Networks

Text classification is a fundamental task in Text Mining (TM) with applications ranging from spam detection to sentiment analysis. One of the current approaches to this task is Graph Neural Network (GNN), primarily used to deal with complex and unstructured data. However, the scalability of GNNs is a significant challenge when dealing with large-scale graphs. Multilevel optimization is prominent among the methods proposed to tackle the issues that arise in such a scenario. This approach uses a hierarchical coarsening technique to reduce a graph, then applies a target algorithm to the coarsest graph and projects the output back to the original graph. Here, we propose a novel approach for text classification using GNN. We build a bipartite graph from the input corpus and then apply the coarsening technique of the multilevel optimization to generate ten contracted graphs to analyze the GNN's performance, training time, and memory consumption as the graph is gradually reduced. Although we conducted experiments on text classification, we emphasize that the proposed method is not bound to a specific task and, thus, can be generalized to different problems modeled as bipartite graphs. Experiments on datasets from various domains and sizes show that our approach reduces memory consumption and training time without significantly losing performance.