论文标题
Rosa：一个可靠的自我对准节点节点图对比度学习的框架
RoSA: A Robust Self-Aligned Framework for Node-Node Graph Contrastive Learning
论文作者
论文摘要
图对比度学习最近取得了重大进展。但是，现有作品很少探索非对准节点节点的对比。在本文中，我们提出了一种名为Rosa的新颖图形对比学习方法，该方法的重点是利用不结盟的增强视图来用于节点级表示学习。首先，我们利用地球推动者的距离来建模最低限度的努力，以将一种视图的分布转换为另一种视图，这是我们的对比目标，这不需要在视图之间对齐。然后，我们将对抗性训练作为一种辅助方法，以增加采样多样性并增强模型的鲁棒性。实验结果表明，Rosa的表现优于同质，非双重动态和动态图的一系列图形对比学习框架，这些框架验证了我们工作的有效性。为了我们的意识，罗莎（Rosa）是第一项工作，重点是非对准节点图形对比度学习问题。我们的代码可在：\ href {https://github.com/zhuyun97/rosa} {\ texttt {https://github.com/zhuyun97/rosa}}}
Graph contrastive learning has gained significant progress recently. However, existing works have rarely explored non-aligned node-node contrasting. In this paper, we propose a novel graph contrastive learning method named RoSA that focuses on utilizing non-aligned augmented views for node-level representation learning. First, we leverage the earth mover's distance to model the minimum effort to transform the distribution of one view to the other as our contrastive objective, which does not require alignment between views. Then we introduce adversarial training as an auxiliary method to increase sampling diversity and enhance the robustness of our model. Experimental results show that RoSA outperforms a series of graph contrastive learning frameworks on homophilous, non-homophilous and dynamic graphs, which validates the effectiveness of our work. To the best of our awareness, RoSA is the first work focuses on the non-aligned node-node graph contrastive learning problem. Our codes are available at: \href{https://github.com/ZhuYun97/RoSA}{\texttt{https://github.com/ZhuYun97/RoSA}}
