Rethinking Causal Relationships Learning in Graph Neural Networks
This work addresses the need for more credible and robust GNNs in machine learning applications by focusing on causal modeling, though it appears incremental as it builds on existing advancements in causal learning for GNNs.
The authors tackled the problem of analyzing and improving causal relationship learning in Graph Neural Networks (GNNs) by constructing a synthetic dataset with known causal structures and introducing a lightweight module, which they validated through experiments on synthetic and real-world datasets, showing effectiveness in enhancing GNNs' causal capabilities.
Graph Neural Networks (GNNs) demonstrate their significance by effectively modeling complex interrelationships within graph-structured data. To enhance the credibility and robustness of GNNs, it becomes exceptionally crucial to bolster their ability to capture causal relationships. However, despite recent advancements that have indeed strengthened GNNs from a causal learning perspective, conducting an in-depth analysis specifically targeting the causal modeling prowess of GNNs remains an unresolved issue. In order to comprehensively analyze various GNN models from a causal learning perspective, we constructed an artificially synthesized dataset with known and controllable causal relationships between data and labels. The rationality of the generated data is further ensured through theoretical foundations. Drawing insights from analyses conducted using our dataset, we introduce a lightweight and highly adaptable GNN module designed to strengthen GNNs' causal learning capabilities across a diverse range of tasks. Through a series of experiments conducted on both synthetic datasets and other real-world datasets, we empirically validate the effectiveness of the proposed module.