In this study, we examine and analyze the behavior of several graph-based models for Bangla text classification tasks. Graph-based algorithms create heterogeneous graphs from text data. Each node represents either a word or a document, and each edge indicates relationship between any two words or word and document. We applied the BERT model and different graph-based models including TextGCN, GAT, BertGAT, and BertGCN on five different datasets including SentNoB, Sarcasm detection, BanFakeNews, Hate speech detection, and Emotion detection datasets for Bangla text. BERT’s model bested the TextGCN and the GAT models by a large difference in terms of accuracy, Macro F1 score, and weighted F1 score. BertGCN and BertGAT are shown to outperform standalone graph models and BERT model. BertGAT excelled in the Emotion detection dataset and achieved a 1%-2% performance boost in Sarcasm detection, Hate speech detection, and BanFakeNews datasets from BERT’s performance. Whereas, BertGCN outperformed BertGAT by 1% for SetNoB, and BanFakeNews datasets while beating BertGAT by 2% for Sarcasm detection, Hate Speech, and Emotion detection datasets. We also examined different variations in graph structure and analyzed their effects.