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

TL;DR

This paper introduces GeoGNN, a manifold-aware graph neural network that mitigates semantic drift in text-attributed graphs by respecting the geometry of pretrained language model embeddings, leading to improved performance.

Contribution

It presents a quantitative framework for measuring semantic drift and proposes a geodesic aggregation mechanism that preserves semantic fidelity on curved manifolds.

Findings

GeoGNN reduces semantic drift in experiments.

It outperforms baseline models on benchmark datasets.

Manifold-aware aggregation improves text-graph learning.

Abstract

Graph neural networks (GNNs) on text--attributed graphs (TAGs) typically encode node texts using pretrained language models (PLMs) and propagate these embeddings through linear neighborhood aggregation. However, the representation spaces of modern PLMs are highly non--linear and geometrically structured, where textual embeddings reside on curved semantic manifolds rather than flat Euclidean spaces. Linear aggregation on such manifolds inevitably distorts geometry and causes semantic drift--a phenomenon where aggregated representations deviate from the intrinsic manifold, losing semantic fidelity and expressive power. To quantitatively investigate this problem, this work introduces a local PCA--based metric that measures the degree of semantic drift and provides the first quantitative framework to analyze how different aggregation mechanisms affect manifold structure. Building upon these insights, we propose Geodesic Aggregation, a manifold--aware mechanism that aggregates neighbor information along geodesics via log--exp mappings on the unit sphere, ensuring that representations remain faithful to the semantic manifold during message passing. We further develop GeoGNN, a practical instantiation that integrates spherical attention with manifold interpolation. Extensive experiments across four benchmark datasets and multiple text encoders show that GeoGNN substantially mitigates semantic drift and consistently outperforms strong baselines, establishing the importance of manifold--aware aggregation in text--attributed graph learning.