Jul 10, 2026, 4:00 AM

Towards the Explainability of Temporal Graph Networks via Memory Backtracking and Topological Attribution

TickrWire Editorial Desk·Jul 10, 2026, 4:00 AM·1 min read AI-assisted, human-reviewed

Reported by arXiv cs.LG: Towards the Explainability of Temporal Graph Networks via Memory Backtracking and Topological Attribution. Analysis and context written by TickrWire.

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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 capt

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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 capt

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