GeoGNN: Quantifying and Mitigating Semantic Drift in Text-Attributed Graphs

The recent publication 'GeoGNN: Quantifying and Mitigating Semantic Drift in Text-Attributed Graphs' highlights a critical challenge in the application of graph neural networks (GNNs) to text-attributed graphs (TAGs). Traditional methods often lead to semantic drift, where the aggregated representations deviate from their original semantic meanings due to linear aggregation on non-linear manifolds. To tackle this, the authors introduce a local PCA-based metric for quantifying semantic drift and propose Geodesic Aggregation, a method that respects the manifold structure of textual embeddings. Extensive experiments conducted across four benchmark datasets demonstrate that GeoGNN significantly mitigates semantic drift and outperforms existing baselines. This work not only advances the understanding of GNNs but also opens new avenues for improving AI applications that rely on text data.