Conflict-Aware Complementary Balancing for Attributed Graph Clustering

Attributed graph clustering (AGC) often combines multiple objectives to learn a shared representation. Although these objectives can provide complementary supervision, their gradients may become incompatible when jointly updating the same encoder, leading to unstable representation evolution and less coherent cluster structure. To address this issue, we propose Conflict-Aware Complementary Balancing (CACB), which explicitly coordinates complementary objectives according to their gradient compatibility during shared-encoder optimization. CACB first introduces objective-specific linear mappings to decouple heterogeneous supervisory signals in dedicated learned spaces, allowing different objectives to preserve their distinct optimization roles. CACB further imposes compatibility constraints on the aggregate encoder gradient. When incompatibility is detected, it formulates gradient correction as a projection that minimizes deviation over an affine update set subject to these constraints, yielding a closed-form update with minimal intervention that optimally repairs first-order incompatibility across objectives. Extensive experiments on six benchmark datasets demonstrate that CACB consistently outperforms competitive attributed graph clustering methods. Code is available at https://anonymous.4open.science/r/CACB-EC44.