This paper proposes a spectral-based graph augmentation framework designed to investigate and improve generalization behavior in dynamic graphs under distribution shifts and develops a multi-encoder architecture in which each encoder targets a specific spectral shift component to generate referential representations.
Qian-Yu Song, Chao Li, Zhongying Zhao et al.· Proceedings of the Thirty-Fi...· 0 citations
This work proposes the SENSE model, a confidence-aware soft sentiment assignment and a sentiment exchange mechanism to quantify fine-grained preferences and explicitly model sentiment interaction, and employs a confidence-aware soft sentiment assignment and a sentiment exchange mechanism to quantify fine-grained prefer...
Ying-Jie Chen, Xiang Li, Dong Chen et al.· Proceedings of the Thirty-Fi...· 0 citations
A novel Dual-Channel Hybrid Graph Neural Network (HyGNN) that jointly models temporal dynamics and high-order structural dependencies and robustly captures the nonlinear interplay between mobility and sociality.
Liang Chen, Xiang Li, Gui-Yuan Jiang et al.· Proceedings of the Thirty-Fi...· 0 citations
This work designs a dual-consistency graph augmentation method that enriches the graph topology to mitigate structural vulnerabilities and presents an adversarial masked autoen-coder to mitigate model overfitting to the perturbed graph.
Qi-Qi Zhang, Chuan-Jin Liu, Gen Liu et al.· Proceedings of the Thirty-Fi...· 0 citations
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