Attributed graph clustering is an unsupervised task that partitions nodes into different groups. Self-supervised learning (SSL) shows great potential in handling this task, and some recent studies simultaneously learn multiple SSL tasks to further boost performance. Currently, different SSL tasks are assigned the same set of weights for all graph nodes. However, we observe that some graph nodes whose neighbors are in different groups require significantly different emphases on SSL tasks. In this paper, we propose to dynamically learn the weights of SSL tasks for different nodes and fuse the embeddings learned from different SSL tasks to boost performance. We design an innovative graph clustering approach, namely Dynamically Fusing Self-Supervised Learning (DyFSS). Specifically, DyFSS fuses features extracted from diverse SSL tasks using distinct weights derived from a gating network. To effectively learn the gating network, we design a dual-level self-supervised strategy that incorporates pseudo labels and the graph structure. Extensive experiments on five datasets show that DyFSS outperforms the state-of-the-art multi-task SSL methods by up to 8.66% on the accuracy metric. The code of DyFSS is available at: https://github.com/q086/DyFSS.

翻译：属性图聚类是一种将节点划分至不同分组的无监督任务。自监督学习在处理该任务中展现出巨大潜力，近期一些研究通过同时学习多个自监督任务进一步提升性能。当前，不同自监督任务对所有图节点采用相同的权重设置。然而，我们观察到，部分邻居节点归属于不同分组的图节点需要显著不同的自监督任务权重侧重。为此，本文提出动态学习不同节点的自监督任务权重，并融合不同自监督任务学习到的嵌入表示以提升性能。我们设计了一种创新的图聚类方法，即动态融合自监督学习（DyFSS）。具体而言，DyFSS通过门控网络生成差异化权重，对来自多种自监督任务的特征进行融合。为有效训练门控网络，我们设计了融合伪标签与图结构的双层自监督策略。在五个数据集上的大量实验表明，DyFSS在准确率指标上相较当前最优的多任务自监督方法最高提升8.66%。DyFSS代码已在https://github.com/q086/DyFSS 开源。