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Haobing Liu

2 accepted papers

2026

Disentangled Hypergraph Network with Implicit Structure Learning for Mobility Social Relationship Inference

IJCAI 2026

Inferring social relationships from users' mobile data holds significant value for personalized recommendations. Most methods model user interactions based on co-occurrence records, achieving impressive success in capturing social signals. However, despite these advancements, current techniques stil

Cited by 0Scholar
2025

MaskDGNN: Self-Supervised Dynamic Graph Neural Networks with Activeness-aware Temporal Masking

IJCAI 2025

Integrating dynamics into graph neural networks (GNNs) provides deeper insights into the evolution of dynamic graphs, thereby enhancing the temporal representation in real-world dynamic network problems. Existing methods extracting critical information from dynamic graphs face two key challenges, ei