  Given the success of Graph Neural Networks (GNNs) for structure-aware machine
learning, many studies have explored their use for text classification, but
mostly in specific domains with limited data characteristics. Moreover, some
strategies prior to GNNs relied on graph mining and classical machine learning,
making it difficult to assess their effectiveness in modern settings. This work
extensively investigates graph representation methods for text classification,
identifying practical implications and open challenges. We compare different
graph construction schemes using a variety of GNN architectures and setups
across five datasets, encompassing short and long documents as well as
unbalanced scenarios in diverse domains. Two Transformer-based large language
models are also included to complement the study. The results show that i)
although the effectiveness of graphs depends on the textual input features and
domain, simple graph constructions perform better the longer the documents are,
ii) graph representations are especially beneficial for longer documents,
outperforming Transformer-based models, iii) graph methods are particularly
efficient at solving the task.
