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Kehan Yin

2 accepted papers

2024

An Efficient Subgraph-Inferring Framework for Large-Scale Heterogeneous Graphs

AAAI 2024technical

Heterogeneous Graph Neural Networks (HGNNs) play a vital role in advancing the field of graph representation learning by addressing the complexities arising from diverse data types and interconnected relationships in real-world scenarios. However, traditional HGNNs face challenges when applied to la…

2024

CausalNET: Unveiling Causal Structures on Event Sequences by Topology-Informed Causal Attention

IJCAI 2024poster

Causal discovery on event sequences holds a pivotal significance across domains such as healthcare, finance, and industrial systems. The crux of this endeavor lies in unraveling causal structures among event types, typically portrayed as directed acyclic graphs (DAGs). Nonetheless, prevailing method…