arXiv:2606.27201v1 Announce Type: new Abstract: Event-based Temporal Graph Neural Networks (ETGNNs) have demonstrated strong performance across a wide range of applications, including social network analysis, epidemic tracing, recommender systems, and political event forecasting. However, their increasing complexity poses significant challenges for explainability. Existing explanation methods focus only on a subset of the information flow within ETGNNs, typically tracing contributions from the event-related embeddings to the output. Consequently, they overlook the important pathways through ev
The increasing complexity and widespread deployment of Temporal Graph Neural Networks (TGNs) in critical applications necessitate enhanced explainability, pushing research in this area.
Improved explainability in advanced AI models like TGNs is crucial for trust, regulation, and identifying subtle biases or vulnerabilities, especially as these models are deployed in high-stakes domains.
Existing explanation methods for TGNs are being advanced to encompass a more complete understanding of information flow, beyond just event-related embeddings, leading to more robust and transparent AI systems.
- · AI developers
- · Regulators
- · Industries using TGNs
- · AI explainability researchers
- · Black-box AI systems
- · Models with untraceable decision pathways
More transparent and debuggable AI systems become available for widespread adoption.
Increased user and regulatory confidence in AI deployment, accelerating the integration of complex AI into sensitive applications.
New standards for AI explainability emerge, potentially influencing the design and development of future AI architectures.
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Read at arXiv cs.LG