论文标题
锥形对准：稳定的网络对齐与邻近节点嵌入
CONE-Align: Consistent Network Alignment with Proximity-Preserving Node Embedding
论文作者
论文摘要
网络对齐是在不同图中查找节点之间的对应关系的过程，具有许多科学和工业应用。现有的无监督网络对准方法找到了次优对准，可以打破节点社区，即不保留匹配的社区一致性。为了改善这一点，我们提出了锥体对齐，该圆锥体对象与节点嵌入的内部近端建模，并在对齐嵌入子空间后使用它们来匹配跨网络的节点。关于不同的，具有挑战性的数据集的实验表明，锥体对齐是强大的，并且比在高度嘈杂的设置中的表现最佳的最先进的图形对准算法平均得出了19.25％的精度。
Network alignment, the process of finding correspondences between nodes in different graphs, has many scientific and industrial applications. Existing unsupervised network alignment methods find suboptimal alignments that break up node neighborhoods, i.e. do not preserve matched neighborhood consistency. To improve this, we propose CONE-Align, which models intra-network proximity with node embeddings and uses them to match nodes across networks after aligning the embedding subspaces. Experiments on diverse, challenging datasets show that CONE-Align is robust and obtains 19.25% greater accuracy on average than the best-performing state-of-the-art graph alignment algorithm in highly noisy settings.
