Research map / Graph learning / Knowledge and temporal graphs
Make Information Diffusion Explainable: LLM-based Causal Framework for Diffusion Prediction
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Proposes an LLM-based causal framework for information diffusion prediction that aims to make the forecasts explainable.
Problem
Predicting which users will be infected in future steps of information diffusion on social platforms, where existing GNN and sequence-model methods focus on structural and temporal patterns and lack explainability.
Approach
Uses an LLM-based causal framework to bring explainability to diffusion prediction beyond structural and temporal patterns; detailed mechanism not stated in the abstract provided.
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