Shao-En Lin — accepted AI-conference papers

2026

Atomic HINs: Entity-Attribute Duality for Heterogeneous Graph Modeling

Shao-En Lin, Ming-Yi Hong, Miao-Chen Chiang, Chih-Yu Wang, Che Lin

ICLR 2026poster

Heterogeneous Information Networks (HINs) provide a powerful framework for modeling multi-typed entities and relations, typically defined under a fixed schema. Yet, most research assumes this structure is given, overlooking the fact that alternative designs can emphasize different aspects of the dat…

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2026

HINPool: A Unified Heterogeneous Graph Pooling Framework for Accurate Molecular and Protein Property Prediction

Ming-Yi Hong, You-Chen Teng, Shao-En Lin, Chih-Yu Wang, Che Lin

AAAI 2026technical

Graph pooling has gained significant progress in recent years as an effective solution for graph-level property classification tasks. With the emergence of research on Heterogeneous Information Networks (HINs), this paper argues that graph-level datasets for graph classification should be treated as

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