arXiv:2606.03839v1 Announce Type: new Abstract: Text-Attributed Graph (TAG) is an important type of graph structured data, where each node has a text description. TAG models usually train a Graph Neural Network (GNN) and language model jointly, which leads to high space and time consumption, especially on large datasets. To mitigate this, we propose TAGSAM, a condensation method that compresses TAGs while preserving training accuracy. TAGSAM comes with two key designs, i.e., subgraph text Selection and Attribute similarity Matching, which compress the text description and graph topology of TAG
The increasing scale and complexity of AI models, particularly those combining graph neural networks and language models, necessitate new methods for computational efficiency and resource management.
This development addresses a fundamental constraint in deploying advanced AI on large datasets, enabling broader application and reducing the significant computational overhead associated with current methods.
The ability to compress text-attributed graphs while maintaining accuracy will make advanced AI models more accessible and cost-effective, expanding their practical applicability beyond highly resourced environments.
- · AI developers and researchers
- · Cloud computing providers (through increased efficiency)
- · Industries with large, complex datasets (e.g., social media, bioinformatics)
- · Inefficient AI model architectures
- · Organizations relying solely on brute-force computational scaling
Reduced computational resource requirements for training complex text-attributed graph models.
Faster development cycles and deployment of AI applications in domains currently constrained by computational cost.
Democratization of advanced AI capabilities, potentially fostering innovation in underserved sectors or regions.
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Read at arXiv cs.LG