Every Node is Different: Dynamically Fusing Self-Supervised Tasks for
  Attributed Graph Clustering
Pengfei Zhu Qian Wang Yu Wang Jialu Li Qinghua Hu
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.