The Road to Explainable Graph Neural Networks

GOAL: Graph Neural Networks (GNNs) are being increasingly adopted in various real-world applications, including drug and material discovery [16, 17, 27], recommendation engines [29], congestion modeling on road networks [5], and weather forecasting [11]. However, similar to other deeplearning models...

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Bibliographic Details
Published in:SIGMOD record 2024-11, Vol.53 (3), p.32-34
Main Author: Ranu, Sayan
Format: Article
Language:English
Subjects:
Computing methodologies
Computing methodologies / Machine learning
Computing methodologies / Machine learning / Learning paradigms
Computing methodologies / Machine learning / Learning paradigms / Supervised learning
Computing methodologies / Machine learning / Machine learning approaches
Computing methodologies / Machine learning / Machine learning approaches / Neural networks
Human-centered computing
Information systems
Information systems / Data management systems
Information systems / Data management systems / Database design and models
Information systems / Data management systems / Database design and models / Graph-based database models
Information systems / Information systems applications
Information systems / Information systems applications / Data mining
Information systems / World Wide Web
Information systems / World Wide Web / Web applications
Information systems / World Wide Web / Web applications / Social networks
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Summary:GOAL: Graph Neural Networks (GNNs) are being increasingly adopted in various real-world applications, including drug and material discovery [16, 17, 27], recommendation engines [29], congestion modeling on road networks [5], and weather forecasting [11]. However, similar to other deeplearning models, GNNs are considered black boxes due to their limited capacity to provide explanations for their predictions. This lack of interpretability poses a significant barrier to their adoption in critical domains such as healthcare, finance, and law enforcement, where transparency and trustworthiness are essential for decision-making processes. In these pivotal domains, understanding the rationale behind model predictions is crucial not only for compliance with interpretability requirements but also for identifying potential vulnerabilities and gaining insights to refine the model further. How do we make GNNs interpretable? This is a central motivating question driving my research pursuits.
ISSN:0163-5808
DOI:10.1145/3703922.3703930