Abstract
Graph representation learning methods have significantly transformed applications in various domains. However, their success often comes at the cost of interpretability, hindering them from being adopted in critical decision-making scenarios. In conventional graph classification, the integration of domain expertise to enhance model training has been underutilized, leading to discrepancies in decision outcomes between humans and models. To address this, we introduce a novel framework involving active human verification in graph classification processes. Our approach features a human-aligned representation learning component, achieved by seamlessly integrating Graph Neural Network architectures and leveraging human domain knowledge and feedback. This framework enhances model transparency and interpretability and fosters collaborative decision-making between humans and AI systems. Extensive evaluations and user studies prove the efficiency of our framework.
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Acknowledgments
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korean government (MSIT)(No. RS-2023-00222663, RS-2023-00262885).
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Bui, TC., Li, WS. (2024). Human-Driven Active Verification for Efficient and Trustworthy Graph Classification. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14645. Springer, Singapore. https://doi.org/10.1007/978-981-97-2242-6_9
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DOI: https://doi.org/10.1007/978-981-97-2242-6_9
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