Integrating structural and semantic signals in text-attributed graphs with BiGTex
Text-attributed graphs (TAGs) pose unique challenges for representation learning by requiring models to capture both the semantic richness of node-associated texts and the structural dependencies of the graph. While graph neural networks (GNNs) effectively model topological information, they are lim...
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| Hauptverfasser: | , |
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| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
Elsevier
2026-06-01
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| Schriftenreihe: | Machine Learning with Applications |
| Schlagworte: | |
| Online-Zugang: | http://www.sciencedirect.com/science/article/pii/S2666827026000861 |
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