Link prediction is a fundamental task in graph machine learning. While Graph Neural Network (GNN)-based methods are mainstream, they exhibit limitations: restricted receptive fields hinder their ability to capture long-range structural information between target node pairs; the node-centric paradigm may lead to information loss as it mismatches the nature of link-centric tasks; and neighborhood node features may introduce task-irrelevant noise in link prediction scenarios, which has been less addressed by existing improvements built on the inherent GNN message-passing paradigm. We propose a Topology-Aware Graph Neural Network (TAGNN) framework for link prediction, achieving breakthroughs from two key perspectives. First, during the GNN neighborhood structure encoding stage, we innovatively propose to discard neighborhood node attributes that may introduce interference in the link-centric task, retaining only topological information encoded via Double-Radius Node Labeling (DRNL) for neighborhood structure encoding. DRNL transforms topological relationships into learnable structural features by computing distances between nodes within the subgraph and the target nodes, enabling the model to focus more precisely on fitting neighborhood topology and avoiding interference from irrelevant features. Second, we design a Reinforced Structural Transformer (RST) module, with its core being the Reinforced Structural Attention (RSA) mechanism. RSA first extracts structural correlation features of target node pairs using topological heuristics like shortest path distance, Adamic-Adar index, and Jaccard coefficient. These features are then fused with node features to form a structural encoding that directly models pairwise relationships, compensating for GNN’s deficiency in long-range structural perception. Crucially, RST confines attention computation strictly to the target node pair rather than all nodes, simultaneously avoiding noise from non-target nodes and reducing the computational complexity from quadratic (as in a standard Transformer) to linear, significantly boosting efficiency. Experiments on six datasets (three from OGB and three classic attributed graphs) demonstrate the outstanding performance of TAGNN, achieving top rankings on ogbl-ppa, ogbl-citation2, and Pubmed. The variant without neighborhood features performs even better. Ablation studies confirm the contribution of RST, and TAGNN shows a 10–16 × computational efficiency gain over a fully-connected Transformer baseline.
Liang et al. (Tue,) studied this question.