论文标题
GraphReach：使用可及性估计的位置感知图神经网络
GraphReach: Position-Aware Graph Neural Network using Reachability Estimations
论文作者
论文摘要
大多数现有的图形神经网络（GNN）都学习编码其本地社区而不是其位置的节点嵌入。因此，两个节点非常遥远，但位于类似的本地社区中，这些节点映射到这些网络中的类似嵌入。此限制阻止了依赖位置信息的预测任务中的准确性能。在本文中，我们开发了GraphReach，这是一种具有位置感知的归纳GNN，该GNN通过相对于一组锚节点的可及性估计来捕获节点的全球位置。从战略上选择锚，以便最大化所有节点的可及性估计。我们表明，这种组合锚定的选择问题是NP-固定的，因此，会产生贪婪（1-1/e）近似启发式。针对最先进的GNN体系结构的经验评估表明，GraphReach可提供高达40％的准确性相对提高。此外，对抗性攻击更为强大。
Majority of the existing graph neural networks (GNN) learn node embeddings that encode their local neighborhoods but not their positions. Consequently, two nodes that are vastly distant but located in similar local neighborhoods map to similar embeddings in those networks. This limitation prevents accurate performance in predictive tasks that rely on position information. In this paper, we develop GraphReach, a position-aware inductive GNN that captures the global positions of nodes through reachability estimations with respect to a set of anchor nodes. The anchors are strategically selected so that reachability estimations across all the nodes are maximized. We show that this combinatorial anchor selection problem is NP-hard and, consequently, develop a greedy (1-1/e) approximation heuristic. Empirical evaluation against state-of-the-art GNN architectures reveal that GraphReach provides up to 40% relative improvement in accuracy. In addition, it is more robust to adversarial attacks.
