We introduce a representation learning framework that provides high-confidence fairness guarantees with controllable error thresholds and confidence levels via adversarial inference.

This is the first work to identify the heterogeneity in temporal interactions of CTTGs and investigate its impact on the performance of temporal link prediction. We propose TAMI, which effectively handles the heterogeneity in temporal interactions.