SCARCE
A label-free graph-level out-of-distribution generalization framework that learns distributionally robust graph representations through two-stage training, comprising VGAE warm-up and adversarial environment confusion, achieving implicit feature reweighting.
Timeline (last 90 days)
The SCARCE framework can learn distributionally robust graph representations using only the environment partition of the training graph (from metadata or unsupervised clustering), without requiring class labels.
SCARCE adopts a two-stage process: in the first stage, a variational graph autoencoder (VGAE) performs warm-up via a graph reconstruction task; in the second stage, an adversarial confusion mechanism is introduced.
In SCARCE's second stage, the discriminator attempts to distinguish the environment from the representations output by the encoder, while the encoder is trained to generate representations that make the discriminator unable to distinguish the environment.
The paper's theoretical analysis shows that environment confusion shrinks the directions in representation space relevant to environment discrimination, which under a regularized downstream classifier is equivalent to imposing a stronger directional penalty on environment-correlated features.
SCARCE implements an implicit feature reweighting: it continuously reduces reliance on spurious correlations through representation learning while not erasing information.
On a controlled synthetic dataset and the DrugOOD dataset, SCARCE achieved state-of-the-art OOD generalization performance among label-free methods.
On DrugOOD, SCARCE does not use class labels and trains using only the environment partition, achieving the best performance compared with label-free baselines.
SCARCE's potential limitations include dependence on the quality of the environment partition: performance may degrade when self-supervised clustering is ineffective; in addition, the computational overhead and stability of adversarial training are also factors to consider in practical deployment.
SCARCE's core contribution lies in freeing graph OOD generalization from label dependence, providing a feasible path for graph learning scenarios with scarce labels.
SCARCE's theoretical connection reveals the equivalence between adversarial confusion and feature reweighting, offering a new perspective for understanding label-free generalization.
All Facts (10)
The SCARCE framework can learn distributionally robust graph representations using only the environment partition of the training graph (from metadata or unsupervised clustering), without requiring class labels.
50%UnverifiedSCARCE adopts a two-stage process: in the first stage, a variational graph autoencoder (VGAE) performs warm-up via a graph reconstruction task; in the second stage, an adversarial confusion mechanism is introduced.
50%UnverifiedIn SCARCE's second stage, the discriminator attempts to distinguish the environment from the representations output by the encoder, while the encoder is trained to generate representations that make the discriminator unable to distinguish the environment.
50%UnverifiedThe paper's theoretical analysis shows that environment confusion shrinks the directions in representation space relevant to environment discrimination, which under a regularized downstream classifier is equivalent to imposing a stronger directional penalty on environment-correlated features.
50%UnverifiedSCARCE implements an implicit feature reweighting: it continuously reduces reliance on spurious correlations through representation learning while not erasing information.
50%UnverifiedOn a controlled synthetic dataset and the DrugOOD dataset, SCARCE achieved state-of-the-art OOD generalization performance among label-free methods.
50%UnverifiedOn DrugOOD, SCARCE does not use class labels and trains using only the environment partition, achieving the best performance compared with label-free baselines.
50%UnverifiedSCARCE's potential limitations include dependence on the quality of the environment partition: performance may degrade when self-supervised clustering is ineffective; in addition, the computational overhead and stability of adversarial training are also factors to consider in practical deployment.
50%UnverifiedSCARCE's core contribution lies in freeing graph OOD generalization from label dependence, providing a feasible path for graph learning scenarios with scarce labels.
50%UnverifiedSCARCE's theoretical connection reveals the equivalence between adversarial confusion and feature reweighting, offering a new perspective for understanding label-free generalization.
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