Cooperative Classification and Rationalization for Graph Generalization
Linan Yue Qi Liu Ye Liu Weibo Gao Fangzhou Yao Wenfeng Li
Graph Neural Networks (GNNs) have achieved impressive results in graph
classification tasks, but they struggle to generalize effectively when faced
with out-of-distribution (OOD) data. Several approaches have been proposed to
address this problem. Among them, one solution is to diversify training
distributions in vanilla classification by modifying the data environment, yet
accessing the environment information is complex. Besides, another promising
approach involves rationalization, extracting invariant rationales for
predictions. However, extracting rationales is difficult due to limited
learning signals, resulting in less accurate rationales and diminished
predictions. To address these challenges, in this paper, we propose a
Cooperative Classification and Rationalization (C2R) method, consisting of the
classification and the rationalization module. Specifically, we first assume
that multiple environments are available in the classification module. Then, we
introduce diverse training distributions using an environment-conditional
generative network, enabling robust graph representations. Meanwhile, the
rationalization module employs a separator to identify relevant rationale
subgraphs while the remaining non-rationale subgraphs are de-correlated with
labels. Next, we align graph representations from the classification module
with rationale subgraph representations using the knowledge distillation
methods, enhancing the learning signal for rationales. Finally, we infer
multiple environments by gathering non-rationale representations and
incorporate them into the classification module for cooperative learning.
Extensive experimental results on both benchmarks and synthetic datasets
demonstrate the effectiveness of C2R. Code is available at
https://github.com/yuelinan/Codes-of-C2R.