no code implementations • 16 Dec 2023 • Ruibin Zeng, Minglong Lei, Lingfeng Niu, Lan Cheng
Then, we further design a pre-training and domain adaptation framework to extract the transferable and generalizable features so that different COs can benefit from them.
no code implementations • 15 Mar 2022 • Hao Jia, Junzhong Ji, Minglong Lei
In this paper, we propose a novel graph neural network based on supervised contrastive learning with structure inference for graph classification.
no code implementations • 2 Dec 2021 • Yating Ren, Junzhong Ji, Lingfeng Niu, Minglong Lei
In this paper, we propose a multi-task self-distillation framework that injects self-supervised learning and self-distillation into graph convolutional networks to separately address the mismatch problem from the structure side and the label side.
no code implementations • 30 Sep 2021 • Minglong Lei, Yong Shi, Lingfeng Niu
To address this issue, we propose a latent network embedding model based on adversarial graph auto-encoders.
no code implementations • Neural Networks 2021 • Junzhong Ji, Ye Liang, Minglong Lei
Modern graph neural networks provide facilitation to jointly capture the above information in attributed graphs with a feature aggregation manner, and have achieved great success in attributed graph clustering.
no code implementations • 9 May 2018 • Yong Shi, Minglong Lei, Peng Zhang, Lingfeng Niu
In order to solve the limitations, we propose in this paper a network diffusion based embedding method.