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％。

每个节点都不同：动态融合自监督任务用于属性图聚类

Every Node is Different: Dynamically Fusing Self-Supervised Tasks for  Attributed Graph Clustering

Attributed graph clustering is challenging as it requires joint modelling of
graph structures and node attributes. Recent progress on graph convolutional
networks has proved that graph convolution is effective in combining structural
and content information, and several recent methods based on it have achieved
promising clustering performance on some real attributed networks. However,
there is limited understanding of how graph convolution affects clustering
performance and how to properly use it to optimize performance for different
graphs. Existing methods essentially use graph convolution of a fixed and low
order that only takes into account neighbours within a few hops of each node,
which underutilizes node relations and ignores the diversity of graphs. In this
paper, we propose an adaptive graph convolution method for attributed graph
clustering that exploits high-order graph convolution to capture global cluster
structure and adaptively selects the appropriate order for different graphs. We
establish the validity of our method by theoretical analysis and extensive
experiments on benchmark datasets. Empirical results show that our method
compares favourably with state-of-the-art methods.

本文提出了一种自适应图卷积方法来处理带属性的图聚类问题，该方法利用高阶图卷积来捕获全局聚类结构，并针对不同的图自适应地选择适当的阶数，而实验结果表明，该方法与现有的最先进方法相媲美。