OCG: Orthogonal Consensus Graph Learning for Robust Heterogeneous Graph Representations

Heterogeneous graph representation learning uses typed relations and node attributes, both of which can be corrupted. We propose Orthogonal Consensus Graph Learning (OCG), which augments contrastive alignment between matched nodes with a residual cross-Gram penalty and stochastic-path predictive consistency. We derive an exact decomposition of the residual penalty into a spectral shrinkage term for the semantic residual and a coupling term for its interaction with relational disagreement. We derive bounds on corruption-induced changes in the semantic residual and the fused representation under directional separation conditions. On ACM, DBLP, Yelp, and MAG, OCG achieves the highest mean Macro-F1 and Micro-F1 among the evaluated baselines. We evaluate robustness under edge and node-feature perturbations, assess individual objectives through ablation, and examine residual geometry in a controlled experiment.