论文标题
异质图蒙版自动编码器
Heterogeneous Graph Masked Autoencoders
论文作者
论文摘要
生成的自我监督学习（SSL），尤其是蒙面的自动编码器，已成为最令人兴奋的学习范式之一，并且在处理图形数据方面表现出了巨大的潜力。但是，现实图形总是异质的，它提出了现有方法忽略的三个关键挑战：1）如何捕获复杂的图形结构？ 2）如何合并各种节点属性？ 3）如何编码不同的节点位置？鉴于此，我们研究了异质图上生成SSL的问题，并提出了HGMAE，这是一种新型的异质图掩盖自动编码器模型，以应对这些挑战。 HGMAE通过两种创新的掩蔽技术和三种独特的培训策略捕获了全面的图形信息。特别是，我们首先使用动态掩蔽率开发Metapath掩盖和自适应属性掩蔽，以实现异质图上有效稳定的学习。然后，我们设计了几种培训策略，包括基于Metapath的边缘重建，以采用复杂的结构信息，目标属性恢复以包含各种节点属性以及位置特征预测以编码节点位置信息。广泛的实验表明，HGMAE在多个数据集的几个任务上都优于对比度和生成的最新基准。代码可在https://github.com/meettyj/hgmae上找到。
Generative self-supervised learning (SSL), especially masked autoencoders, has become one of the most exciting learning paradigms and has shown great potential in handling graph data. However, real-world graphs are always heterogeneous, which poses three critical challenges that existing methods ignore: 1) how to capture complex graph structure? 2) how to incorporate various node attributes? and 3) how to encode different node positions? In light of this, we study the problem of generative SSL on heterogeneous graphs and propose HGMAE, a novel heterogeneous graph masked autoencoder model to address these challenges. HGMAE captures comprehensive graph information via two innovative masking techniques and three unique training strategies. In particular, we first develop metapath masking and adaptive attribute masking with dynamic mask rate to enable effective and stable learning on heterogeneous graphs. We then design several training strategies including metapath-based edge reconstruction to adopt complex structural information, target attribute restoration to incorporate various node attributes, and positional feature prediction to encode node positional information. Extensive experiments demonstrate that HGMAE outperforms both contrastive and generative state-of-the-art baselines on several tasks across multiple datasets. Codes are available at https://github.com/meettyj/HGMAE.
