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continuous-time dynamic graph forecasting

topic1 events
papersSEP 10 04:00 UTC

LiFTER: A Neuro-Symbolic Method for Interpretable Continuous-Time Graph Forecasting

Researchers introduce LiFTER, a neuro-symbolic framework aimed at making continuous-time dynamic graph forecasting more transparent. Instead of leaving link predictions hidden inside opaque neural states that compress past interactions, the method surfaces which entities are shared across events and how temporal patterns contribute to forecasts. A revised version (v2) of the preprint has been posted on arXiv.

interpretability/neuro-symbolic/graphs/forecasting