Learning node embeddings that capture a node's position within the broader
graph structure is crucial for many prediction tasks on graphs. However,
existing Graph Neural Network (GNN) architectures have limited power in
capturing the position/location of a given node with respect to all other nodes
of the graph. Here we propose Position-aware Graph Neural Networks (P-GNNs), a
new class of GNNs for computing position-aware node embeddings. P-GNN first
samples sets of anchor nodes, computes the distance of a given target node to
each anchor-set,and then learns a non-linear distance-weighted aggregation
scheme over the anchor-sets. This way P-GNNs can capture positions/locations of
nodes with respect to the anchor nodes. P-GNNs have several advantages: they
are inductive, scalable,and can incorporate node feature information. We apply
P-GNNs to multiple prediction tasks including link prediction and community
detection. We show that P-GNNs consistently outperform state of the art GNNs,
with up to 66% improvement in terms of the ROC AUC score.

本文提出 P-GNN 方法，通过采样锚节点集并计算给定目标节点相对于所有锚节点的距离，从而学习距离加权聚合方案以产生具有位置感知能力的节点嵌入。实验结果表明该方法能有效地提升节点在图结构中的位置特征提取能力，达到了较好的实验效果。