Sparse Subgraph Prediction Based on Adaptive Attention

Link prediction is a crucial problem in the analysis of graph-structured data, and graph neural networks (GNNs) have proven to be effective addressing this problem. However, computational temporal costs associated with large-scale graphs remain concern. This study introduces novel method for link called Sparse Subgraph Prediction Based on Adaptive Attention (SSP-AA). The generates sparse subgraphs utilizes Graph SAmple aggreGatE (GraphSAGE) prediction, aiming reduce computation time while providing foundation future exploration graphs. Certain key issues GraphSAGE are addressed by integrating an adaptive attention mechanism jumping knowledge module into model. To address issue weight distribution GraphSAGE, aggregation function employed, which based mechanism. modification enables model distribute weights adaptively among neighboring nodes, significantly improving its ability capture node relationships. Furthermore, tackle common over-smoothing GNNs, integrated, enabling information sharing across different layers flexibility select appropriate representation depth specific situation. By enhancing quality representations, SSP-AA further boosts performance various tasks involving data.