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: this https URL

本文提出了一种创新的图聚类方法，名为动态融合自监督学习（DyFSS），通过动态学习不同节点的自监督学习任务的权重，并融合不同自监督学习任务的特征来提高性能。通过在五个数据集上进行广泛实验，DyFSS 在准确性指标上超过了最先进的多任务自监督学习方法，最高可达 8.66％。