LiFTER predicts future links in continuous-time dynamic graphs with a signed sum of grounded temporal rule executions, matching neural baselines on historical-negative forecasting while providing exact, replayable evidence for every score.
Self-explainable temporal graph networks based on graph information bottleneck,
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LiFTER: A Grounded Neuro-Symbolic Microscope for Continuous-Time Dynamic Graph Forecasting
LiFTER predicts future links in continuous-time dynamic graphs with a signed sum of grounded temporal rule executions, matching neural baselines on historical-negative forecasting while providing exact, replayable evidence for every score.