Towards the Explainability of Temporal Graph Networks via Memory Backtracking and Topological Attribution
arXiv:2607.07716v1 Announce Type: new Abstract: Temporal graphs are ubiquitous in real-world applications and Temporal Graph Networks (TGNs) have achieved superior predictive accuracy. Understanding which historical events drive model predictions can enhance trustworthiness of TGNs. Existing explanation methods overlook the memory module, the core component that records and updates node histories, leaving the influence of past events unexplored. To address this, we attribute TGNs predictions through the topology attribution tree and memory backtracking tree. The topology attribution tree captu
The increasing complexity and adoption of Temporal Graph Networks in real-world applications highlights the urgent need for robust explainability methods to build trust and facilitate debugging.
Improving the explainability of advanced AI models like TGNs is crucial for their ethical deployment, regulatory compliance, and broader societal acceptance, particularly in sensitive domains.
This research introduces methods to understand how historical data captured in a TGN's memory influences its predictions, providing a novel way to interpret complex temporal AI decisions.
- · AI developers
- · Regulatory bodies
- · Industries using TGNs (e.g., finance, healthcare)
- · Black-box AI models
- · Systems with high explainability demands that lack such features
Increased trust and adoption of advanced temporal AI models due to enhanced transparency.
Development of industry standards and regulatory frameworks requiring explainability features for AI systems.
Shift in AI research priorities towards explainable and interpretable model architectures from their inception.
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Read at arXiv cs.LG