论文标题
带上您自己的视图：图形神经网络，用于链接预测与个性化子图选择
Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection
论文作者
论文摘要
图神经网络（GNN）在链接预测（GNNLP）任务中取得了显着成功。现有的努力首先要预定整个数据集的子图，然后通过利用固定子图引起的邻域结构来应用GNN来编码边缘表示。 GNNLP方法的突出性显着依赖于Adhoc子图。由于实际图中的节点连接性很复杂，因此所有边缘的一个共享子图都受到限制。因此，应将子图的选择个性化为不同的边缘。但是，执行个性化子图的选择是并非繁琐的，因为潜在的选择空间呈指数增长到边缘的尺度。此外，在链接预测场景中培训期间，推理边缘不可用，因此选择过程需要归纳。为了弥合差距，我们将个性化子图选择器（PS2）作为插件框架引入，以自动，亲自和感应地识别执行GNNLP时不同边缘的最佳子图。 PS2被实例化为双层优化问题，可以有效地不同地解决。将GNNLP模型与PS2耦合，我们建议一个针对GNNLP训练的全新角度：首先识别边缘的最佳子图；然后专注于使用采样子图训练推理模型。全面的实验认可我们提出的方法在各种GNNLP骨架（GCN，GraphSage，NGCF，LightGCN和Seal）和多种基准测试（Planetoid，OGB和建议数据集）上的有效性。我们的代码可在\ url {https://github.com/qiaoyu-tan/ps2}上公开获得
Graph neural networks (GNNs) have received remarkable success in link prediction (GNNLP) tasks. Existing efforts first predefine the subgraph for the whole dataset and then apply GNNs to encode edge representations by leveraging the neighborhood structure induced by the fixed subgraph. The prominence of GNNLP methods significantly relies on the adhoc subgraph. Since node connectivity in real-world graphs is complex, one shared subgraph is limited for all edges. Thus, the choices of subgraphs should be personalized to different edges. However, performing personalized subgraph selection is nontrivial since the potential selection space grows exponentially to the scale of edges. Besides, the inference edges are not available during training in link prediction scenarios, so the selection process needs to be inductive. To bridge the gap, we introduce a Personalized Subgraph Selector (PS2) as a plug-and-play framework to automatically, personally, and inductively identify optimal subgraphs for different edges when performing GNNLP. PS2 is instantiated as a bi-level optimization problem that can be efficiently solved differently. Coupling GNNLP models with PS2, we suggest a brand-new angle towards GNNLP training: by first identifying the optimal subgraphs for edges; and then focusing on training the inference model by using the sampled subgraphs. Comprehensive experiments endorse the effectiveness of our proposed method across various GNNLP backbones (GCN, GraphSage, NGCF, LightGCN, and SEAL) and diverse benchmarks (Planetoid, OGB, and Recommendation datasets). Our code is publicly available at \url{https://github.com/qiaoyu-tan/PS2}
