GRAPH-RELATED PREDICTIVE AND TEXT GENERATIVE TASKS

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Challenge:	Real-world data combines structured and unstructured formats, capturing explicit relationships and implicit semantic interdependencies.
Approach:	They propose GraphAgent, an automated agent pipeline addressing both explicit and implicit graph-enhanced semantic dependencies for predictive and generative tasks.
Outcome:	Extensive experiments on diverse datasets validate GraphAgent’s effectiveness in graph-related predictive and text generative tasks.

Papers with graph-related predictive and text generative tasks