no code implementations • 6 Nov 2023 • Zeyuan Zhao, Qingqing Ge, Anfeng Cheng, Yiding Liu, Xiang Li, Shuaiqiang Wang
In addition, most of them only consider the interactions between nodes while neglecting the high-order information behind the latent interactions among different node features.
no code implementations • 3 Nov 2023 • Qingqing Ge, Jianxiang Yu, Zeyuan Zhao, Xiang Li
To further leverage the information of clean labels in the noisy label set, we put forward LNP-v2, which incorporates the noisy label set into the Bayesian network to generate clean labels.
no code implementations • 26 Oct 2023 • Qingqing Ge, Zeyuan Zhao, Yiding Liu, Anfeng Cheng, Xiang Li, Shuaiqiang Wang, Dawei Yin
Graph Neural Networks (GNNs) are powerful in learning semantics of graph data.
1 code implementation • 28 Dec 2022 • Jianxiang Yu, Qingqing Ge, Xiang Li, Aoying Zhou
In addition, we propose a variant model AdaMEOW that adaptively learns soft-valued weights of negative samples to further improve node representation.