Knowledge Graphs (KGs) are a rich source of structured data, and graph neural networks (GNNs) are the dominant tool for learning over them. However, existing message-passing GNNs struggle to scale to large KGs because they rely on the iterative message passing process to learn graph structure. This process is inefficient, especially under mini-batch training, where a node sees only a partial view of its neighborhood. In this paper, we address this problem and present gHAWK, a novel and scalable GNN training framework for large KGs. The key idea is to precompute structural features for each node that capture its local and global structure before GNN training even begins. Specifically, gHAWK introduces a preprocessing step that computes: (a)~Bloom filters to compactly encode local neighborhood structure, and (b)~TransE embeddings to represent each node's global position in the graph. These features are then fused with any domain-specific features (e.g., text embeddings), producing a node feature vector that can be used with any GNN backbone. Equipped with these structural priors, the GNN no longer needs to rediscover graph structure, significantly improving convergence and model accuracy with little memory overhead. Extensive experiments on large datasets from the Open Graph Benchmark demonstrate that gHAWK improves accuracy on both node property prediction and link prediction tasks. Remarkably, gHAWK enables simple GNNs to match or exceed more complex relation-aware ones, suggesting that careful preprocessing can substitute for architectural complexity in KG learning.

翻译：暂无翻译