Why Multi-Objective Graph SSL Training Is Brittle: Three Recurring Failure Modes Explained
burkov · x · 2026-10-19
Graph self-supervised learning is now standard for building representations with scarce labels, with a toolbox spanning contrastive learning, reconstruction, and mutual-information maximization—but the best objective depends on dataset, task, and training stage. Combining several objectives at once is harder than it looks: most pipelines blend all objectives at every update, forcing each step into a compromise. The article identifies three recurring failure modes—objectives that disagree and cancel each other out, objectives whose usefulness drifts over training, and objectives that get quietly starved of attention—making multi-objective training brittle, and sets out to fix this.
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