  Temporal knowledge graphs (TKGs) have shown promise for reasoning tasks by
incorporating a temporal dimension to represent how facts evolve over time.
However, existing TKG reasoning (TKGR) models lack explainability due to their
black-box nature. Recent work has attempted to address this through customized
model architectures that generate reasoning paths, but these recent approaches
have limited generalizability and provide sparse explanatory output. To enable
interpretability for most TKGR models, we propose GradXKG, a novel two-stage
gradient-based approach for explaining Relational Graph Convolution Network
(RGCN)-based TKGR models. First, a Grad-CAM-inspired RGCN explainer tracks
gradients to quantify each node's contribution across timesteps in an efficient
"explain-per-use" fashion. Second, an integrated gradients explainer
consolidates importance scores for RGCN outputs, extending compatibility across
diverse TKGR architectures based on RGCN. Together, the two explainers
highlight the most critical nodes at each timestep for a given prediction. Our
extensive experiments demonstrated that, by leveraging gradient information,
GradXKG provides insightful explanations grounded in the model's logic in a
timely manner for most RGCN-based TKGR models. This helps address the lack of
interpretability in existing TKGR models and provides a universal explanation
approach applicable across various models.
