  Multi-relational graph clustering has demonstrated remarkable success in
uncovering underlying patterns in complex networks. Representative methods
manage to align different views motivated by advances in contrastive learning.
Our empirical study finds the pervasive presence of imbalance in real-world
graphs, which is in principle contradictory to the motivation of alignment. In
this paper, we first propose a novel metric, the Aggregation Class Distance, to
empirically quantify structural disparities among different graphs. To address
the challenge of view imbalance, we propose Balanced Multi-Relational Graph
Clustering (BMGC), comprising unsupervised dominant view mining and dual
signals guided representation learning. It dynamically mines the dominant view
throughout the training process, synergistically improving clustering
performance with representation learning. Theoretical analysis ensures the
effectiveness of dominant view mining. Extensive experiments and in-depth
analysis on real-world and synthetic datasets showcase that BMGC achieves
state-of-the-art performance, underscoring its superiority in addressing the
view imbalance inherent in multi-relational graphs. The source code and
datasets are available at https://github.com/zxlearningdeep/BMGC.
