arXiv:2606.23694v1 Announce Type: new Abstract: Graph-based text classification models typically rely on local neighborhood aggregation and overlook global community structure, despite semantic document graphs exhibiting strong class-consistent clustering. Ignoring this can blur class boundaries and lead to over-smoothing. We propose ModTGCN, a modularity-aware graph neural network for text classification that jointly optimizes cross-entropy and a modularity-based auxiliary objective to promote class-coherent document communities while preserving discriminative representations. The modularity
The continuous evolution of graph neural networks and deep learning methods drives innovation in text classification, addressing limitations of current approaches.
Improving text classification accuracy through modularity-aware GNNs enhances the performance of many AI applications, from search to content analysis and agentic systems.
This research offers a more robust method for text classification by addressing the over-smoothing problem inherent in many GNNs, potentially leading to more reliable AI outputs.
- · AI developers
- · NLP researchers
- · Data scientists
- · Traditional GNN models without modularity awareness
- · Systems relying on less accurate text classification
More accurate and nuanced understanding of textual data will be possible.
This could lead to advancements in AI agent performance as their comprehension of unstructured data improves.
Enhanced text classification may enable more sophisticated and context-aware automated decision-making processes across various industries.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL