1 code implementation • 6 Nov 2023 • Zeyuan Zhao, Qingqing Ge, Anfeng Cheng, Yiding Liu, Xiang Li, Shuaiqiang Wang
However, different types of nodes in heterogeneous graphs have diverse features, it is also necessary to capture interactions among node features, namely the high-order information from feature-level aspect.
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.