GradXKG: A Universal Explain-per-use Temporal Knowledge Graph Explainer
Chenhan Yuan, Hoda Eldardiry

TL;DR
GradXKG introduces a gradient-based, universal explanation method for RGCN-based temporal knowledge graph reasoning models, enhancing interpretability by identifying critical nodes over time with efficient, model-agnostic explanations.
Contribution
It proposes a novel two-stage gradient-based explanation framework, GradXKG, that is applicable across diverse RGCN-based TKGR models, improving interpretability and generalizability.
Findings
Provides insightful, timely explanations grounded in model logic.
Demonstrates effectiveness across various RGCN-based TKGR models.
Addresses the interpretability gap in existing temporal knowledge graph reasoning models.
Abstract
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,…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Explainable Artificial Intelligence (XAI) · Topic Modeling
MethodsConvolution · Relational Graph Convolution Network
